Kévin Darty

Julien Saunier

Nicolas Sabouret

Agents behavior semi-automatic analysis through their comparison to human behavior clustering

Keywords: Virtual autonomous agent, virtual environment, behavior analysis, clustering and aggregation, logs explicitation

This paper presents a generic method to evaluate virtual agents that aim at reproducing humans behaviors in an immersive virtual environment. We first use automated clustering of simulation logs to extract humans behaviors. We then propose an aggregation of the agents logs into those clusters to analyze the credibility of agents behaviors in terms of capacities, lacks, and errors by comparing them to humans ones. We complete this analysis with a subjective evaluation based on a questionnaire filled by human annotators to draw categories of users, making their behaviors explicit. We illustrate this method in the context of immersive driving simulation.

Introduction

Intelligent virtual agents (IVAs) are used in several fields such as crowd simulation [START_REF] Bosse | Agentbased analysis of patterns in crowd behaviour involving contagion of mental states[END_REF] and virtual human listener [START_REF] Gratch | Creating rapport with virtual agents[END_REF]. In these simulations, agents have to produce realistic behaviors. The notion of behavior can cover different views, from low level actions (e.g. action units on human face [START_REF] Courgeon | Multimodal affective and reactive character[END_REF]) to complex emerging movements in crowds [START_REF] Bosse | Agentbased analysis of patterns in crowd behaviour involving contagion of mental states[END_REF]. One specific aspect of IVAs is that they interact directly with human users in virtual environments (VEs). In this context, providing realistic behavior is a key issue to avoid breaking immersion in the VE [START_REF] Fontaine | The experience of a sense of presence in intercultural and international encounters[END_REF].

In the domain of IVAs, several studies have already addressed the questions of believability or credibility of IVAs behaviors. For instance, Campano et al. [START_REF] Campano | An evaluation of the cor-e computational model for affective behaviors[END_REF] proposed evaluation methods for affective models. Pelachaud et al. [START_REF] Pelachaud | Modelling multimodal expression of emotion in a virtual agent[END_REF] proposed a credibility evaluation of the agent affective behavior model. These methods rely on evaluation studies using participants judgment of the agent behavior credibility. Only few research rely on "objective" analysis of simulation data, and are mostly coming from the multi-agent systems (MAS ) domain (e.g. [START_REF] Caillou | Simanalyzer: Automated description of groups dynamics in agent-based simulations[END_REF]) in which the interaction context is very different.

We propose a method for the analysis of the agents credibility that combines human expertise and simulation logs analysis. We consider the specific case of agents aiming at reproducing human behaviors in an immersive VE. We propose to analyze the agents behaviors in terms of capacities, lacks, and errors with respect to humans. First, human participants act in the VE. Their behavior is logged and analyzed using objective methods from AI. IVAs are then evaluated by comparing their behaviors with the human participants ones in the same situation. We complete this analysis with a Human Sciences evaluation.

The next section presents related works in the domain of objective and subjective evaluation that was used in our research. Section 3 presents our method based on data clustering and aggregation algorithms. Section 4 illustrates the potential of this method in the context of a driving simulation.

Related works

In our work, we want to evaluate the agents behavior at a strategic level: we consider that the behavior is based on a choice of tactics and that it evolves according to the dynamics of the environment, and to the mental state of the person [START_REF] Fisher | Handbook of driving simulation for engineering, medicine, and psychology[END_REF]. For this reason, we will distinguish action logs, which are only traces of the behavior, from the behavior itself as it can be analyzed by a human. The work presented in this section relate to this level of behavior.

Objective Approach

Analysis of simulation data for the evaluation of the behavior credibility is widely used in the field of MAS. It consists in verifying through quantitative data that agents behave as in a "real" situation. This validation method is generally used at the macroscopic level [START_REF] Bosse | Agentbased analysis of patterns in crowd behaviour involving contagion of mental states[END_REF]. However, having a valid collective behavior does not imply that the individuals behaviors are realistic. This is the reason why other researchers proposed to focus on the validation at the microscopic level. Caillou [START_REF] Caillou | Simanalyzer: Automated description of groups dynamics in agent-based simulations[END_REF] showed that data analysis is more complex at this scale and cannot be done directly on the simulation logs due to the semantic gap between the noisy raw data and the sought behaviors. Field experts are generally consulted to determine high-level variables that describe the behavior to be analyzed through the data. An automated clustering algorithm can then be used to classify the agents behaviors [START_REF] Caillou | Simanalyzer: Automated description of groups dynamics in agent-based simulations[END_REF]. For generic methods, as one does not have any information on the domain-specific behaviors, they are unpredefined. Therefore, the clustering method has to be unsupervised with a free number of clusters.

It is also worth noting that, in the domain of interaction, Delaherche and Chetouani proposed behavior traces clustering methods for the study of synchrony [START_REF] Delaherche | Interpersonal synchrony: A survey of evaluation methods across disciplines[END_REF]. However, their goal is not to evaluate the realism of an IVA.

The main limitation of this approach is that while it allows to see the difference between categories of behaviors, extracted from the logs (i.e. behavior log clusters), it does not provide information beyond the used variables: it cannot give a meaning to the obtained clusters. On the contrary, the subjective approach, which relies on a higher-level analysis, offers this possibility.

Virtual agents analysis

Errors Abilities Lacks P a rt ic ip a n ts Ag en ts

Aggregation

Objective approach

Subjective approach

Fig. 1: Behavior analysis and evaluation method.

Subjective approach

The subjective approach for the evaluation of behavior similarity with human beings has been widely used in the domain of IVAs [START_REF] Gratch | Evaluating a computational model of emotion[END_REF][START_REF] Pelachaud | Modelling multimodal expression of emotion in a virtual agent[END_REF]. It consists in evaluating immersion quality through questionnaires. When it comes to IVAs and behavior analysis, the studies focus on the behavioral credibility [START_REF] Lester | The persona effect: affective impact of animated pedagogical agents[END_REF], i.e. the evaluation of how human and IVA behaviors appear similar. In this approach, the IVAs are observed by the immersed participant. The strength of this subjective approach is to characterize the adopted behaviors via questionnaires and to catch high-level behaviors through human participants annotations. One can regroup these behaviors based on these high-level descriptions. We shall then speak of annotated behavior clusters.

However, it is difficult to process hundreds of agents with such a method, since it requires a strong involvement of human participants.

Both logs clusters and annotations clusters aim at evaluating the adopted behaviors. For this reason, objective and subjective approaches through simulation data analysis and human expertise, complement each other. In our method, we propose to combine them: we use automated data analysis and aggregation method to build behavior log clusters, and human observers fill out a questionnaire about the adopted behaviors to build annotated behavior clusters.

Behavior analysis and evaluation method

The method we propose is based on the combination of simulation logs analysis (objective part) and answers to a behavior questionnaire (subjective part). The simulation data are classified into behavior logs clusters. The behavior questionnaire allows us to define situation-specific users categories for both participants and agents. We then evaluate the agents by analyzing the behaviors logs clustering composition and make the behaviors explicit via the annotation clusters.

The general method is described in the Figure 1. It consists of 5 main steps: 1) collection of data in simulation and 2) annotation of these data, 3) data preprocessing and automatic clustering, then 4) clusters comparison, and finally 5) composition analysis and explicitation.

In the following subsections, we present these different steps.

Steps 1 and 2: experiments

We use the human behaviors as the reference behaviors to analyze agents ones. This is why the first step of our method is the collection of quantitative data about human participants from an immersive simulation in a VE. We also produce new simulations in which the participant is replaced by an agent (i.e. placed in an identical situation to that presented to participants) and then collect the very same logs as for the participants. Different types of agents are generated by exploring the parameter space such as normativity, experience, decision parameters. . . We call main actors both humans and IVAs gathered together. The raw data from main actors experiments in the simulator are called simulation logs.

The second step is the subjective evaluation of the main actors behaviors. A different set of participants annotates the video replays of all main actors simulations via the behavior questionnaire. This step produces a set of annotations.

Step 3a: logs clustering

The first objective of the third step is to compare participants behaviors and agents behaviors so as to report on the capability of the agents to reproduce human behaviors. Our goal is to compute behavior categories that serve as abstractions to the logs: each cluster shall regroup different logs representative of the same type of high level behavior (see Figure 1).

To begin with, field experts are consulted to identify important domainspecific indicators. The values for these indicators are computed from the simulation logs and then turned into scalars within a series of preprocessing as described on the left part of figure 2. The reason for this pre-processing it that most of the indicators are temporal and an identical behavior adopted by several main actors can occur with a temporal offset. In order to take this into account, we use the Dynamic Time Warping [START_REF] Salvador | Toward accurate dynamic time warping in linear time and space[END_REF] algorithm (DTW ) which computes mutual distances. The K-means algorithm need the data to be in a dimensional space for which axes are perpendicular. So as to include DTW similarities as new variables describing the main actors, we use a Multi-Dimensional Scaling algorithm (MDS ) to place each main actor in a dimensional space. We then process a Principal Component Analysis to project the data on a hyperplane with non-correlated axes. The outcome of this process is a set of indicators. In order to draw the behavior categories, we use an automatic unsupervised clustering algorithm on these indicators. The number of clusters is not defined a priori: we apply the K-means clustering algorithm on the participants indicators, and use the Variance Ratio Criterion [START_REF] Caliński | A dendrite method for cluster analysis[END_REF] to determine the appropriate number of clusters K.

This first part of step 3 has already been published and more information can be found in [START_REF] Darty | A method for semi-automatic explicitation of agent's behavior: application to the study of an immersive driving simulator[END_REF]. In this paper, we add a further building block to this method: the aggregation of agents to the participants clusters.

Step 3b: cluster aggregation

During the clustering process, the addition of a main actor modifies the clusters shape and may change the affectations. However, agents and humans should not be considered with the same view, since the humans behaviors represent the target to which we want to compare our agents behaviors. For this reason, we do not want agents to modify the humans clustering. In order to keep the human clusters intact, the k-means algorithm is applied on participants only. The agents are then aggregated to the fixed human clusters if close enough or classified into new agents clusters.

Our method works as follows. We define for each participants cluster C i ∈ C a threshold t i above which the agent a is considered as being too far from the centroid m i to be aggregated. This threshold t i is defined on each dimension (i.e. on each indicator ind) as the distance between the centroid m i and the farthest participant p:

t ind i = ∀ p, max(|m ind i -p ind |).
In order to allow the aggregation of the near neighbors, we enlarge t i by a percentage of the mean of all thresholds t i , based on a tolerance rate ǫ: this allows to have singleton clusters (for which t i = 0) attracting other main actors.

Each agent a is aggregated to the participants cluster C i of which the centroid is the closest among those under the threshold t i for each dimension. If some agent(s) did not aggregate to any participants cluster due to the thresholds, the first "remaining" agent creates its own cluster C k+1 which is added to the clusters set C so that remaining agents can aggregate to it. Similarly, each remaining agent tries to aggregate to one of the remaining agents clusters, following the same threshold rule as for human clusters, or creates its own cluster if this is not possible. Thus, as shown in Figure 2, we obtain a clustering composed of all the main actors.

Step 3c: annotation clustering

The second objective is to analyze the behaviors through annotations, following the subjective approach. We use the same methodology as for logs clustering: identification of key indicators, unsupervised clustering on those indicators for the human participants and aggregation of IVAs to the human clusters.

The subjective approach requires manual annotation of replays: when the number of IVAs increases, it becomes impracticable to annotate them all. Yet, under the hypothesis that agents aggregated to a logs cluster of participants should have been annotated in the same way as the participants of this cluster, it is still possible to make these agents behaviors explicit via the participants annotation. In this case, the combination of the objective approach and the subjective one allows us to compare any number of agents and any number of agent models with human participants. However, clusters composed only of agents can no longer be explicited. For this reason, in the experiment presented hereafter, and considering that we had only a limited number of agents, we used manual annotation of all main actors.

The second difficulty for annotation clustering is to choose the right set of indicators. We want these indicators to be both field-specific and situation-specific. In general, behavior questionnaires allow to characterize the participant general behavior, while the participant can adopt a specific (and different) behavior in a local situation. For this reason, as will be shown in the next section, we adapt domain-specific behaviors questionnaires to define the indicators for the situation annotation. Scale scores of questionnaires are calculated by adding the scale-related questions, and normalized between 0 and 1.

We then classify the participants scores using the K-means algorithm with the agents aggregation, which builds our annotation clusters.

Steps 4 and 5: clusters analysis

The fourth step of our method is the comparison of the two clusterings (logs clusters with annotations clusters). As both evaluate behaviors, having a strong similarity between them in terms of composition is a partial verification that the logs clustering is meaningful in terms of situation-specific user categories, and thus corresponds to task-related high-level behaviors. We evaluate the similarity between them (dashed arrow) with the Rand Index (RI ).

The fifth and final step consists in analyzing the IVAs. It is possible to distinguish three cluster types in terms of participant and agent composition: 1) The clusters containing both humans and agents : They correspond to highlevel behaviors that are correctly reproduced by the agents. 2) Those consisting of simulated agents only: They correspond to behaviors that were produced only by the agents. In most cases it reflects simulation errors, but it can also be due to a too small participants sample. 3) Those consisting of participants only: They correspond to behaviors that have not been replicated by the agents. Thus, they are either lacks in the agent model or due to a too small agents sample in the parameter space. We then combine this human-agent comparison with the

Evaluation

This section illustrates our method with an application to the study of driving simulation, and then presents the data analysis and discusses the results.

Case study

We used the ARCHISIM road traffic simulator [START_REF] Champion | Behavioral road traffic simulation with archisim[END_REF]. We want to evaluate the realism and credibility of the agents driving behaviors. To do this, the participants drive a car on a road containing simulated vehicles. The circuit (shown in Figure 3b) provides a driving situation on a single carriage way with two opposing lanes. It corresponds to about 1 minute of driving. The main actor encounters a vehicle at low speed on the right lane and oncoming vehicles on the left lane with increasing distances between them.

The Driving Behavior Questionnaire (DBQ) [START_REF] Reason | Errors and violations on the roads: a real distinction?[END_REF] collects data about drivers habits. In order to have a situation-specific questionnaire, we chose to base the annotation on the DBQ scales. The annotation questionnaire provides 5 Likert-type scales: slips, lapses, mistakes, unintended and deliberate violations. In addition, it supplies a scale related to the accident risk.

During the simulation we collect the main actors logs. We collect each 300 ms several variables such as the cap to the lane axis, the speed, and the topology. The road traffic experts chose both high-level indicators (e.g. the inter-vehicles distance, the time to collision, and the number of lane changings) and low-level variables (like speed, acceleration, and lateral distance).

The 22 participants of our driving simulation experiment are regular drivers aged from 24 to 59. Our experiment is carried out on a device comprising a steering wheel, a set of pedals, a gearbox and 3 screens (see Figure 3a).

Firstly, a test without simulated traffic is performed for the participant to get accustomed to the VE. Then, the participant performs the scenario, this time in interaction with simulated vehicles. It should be noted that as the behavior of simulated vehicles is not scripted, situations differ more or less depending on the main actors behavior. The data are then recorded for the processing phase and logs cluster 1

logs cluster 2 (P=3) (A=5, P=2) (P=3) (P=4) (A=9, P=4) (P=6) a1 a2 a4 a3 a2 a3
Fig. 4: Comparison of main actors between logs clustering (with participants P and agents A), and annotations clustering grouped together with the cluster numbers (a#) being written just bellow the composition. a video is made for the replay. Finally, 6 other participants fills the annotations questionnaire after viewing the video replays (22 participants and 14 agents).

Results

We have compared the logs clustering and the annotations clustering (see Figure 4). The Rand Index between the two clusterings is 0.51. There are 2 behavior log clusters and 4 behavior annotation clusters.

Logs cluster 1 contains 9 participants and all the 14 agents. The number of lane changings indicator value is 0 meaning that these main actors did not try to overtake the vehicle at low speed and preferred to follow it. As this cluster is composed of both agents and participants, it is therefore a capacity of the agent model to reproduce a human behavior which is to choose not to overtake.

Logs cluster 2 is only composed of participants. These 13 participants overtake the vehicle at low speed after the 2 nd or the 3 rd oncoming vehicle. As there are only participants, this human behavior can be considered as a lack in the agent model: the agents cannot choose to overtake as some human do.

Annotation cluster 1 contains 3 participants and no agent. The annotators consider that they are the more dangerous drivers with very high scores on each scale and especially on the judgment scale. Since no agent was considered that dangerous, and as the aim of the agents is to reproduce the most complete panel of human behaviors, there is a lack of unsafe behaviors in the model.

Annotation cluster 2 is composed of both participants and agents. They are annotated as very cautious drivers with the smallest scores on each scale. The space parameter of these agents ensure more respect for the highway code and smoother driving. The normative human behavior can therefore be considered as partly reproduced.

Annotation cluster 3 is a smaller cluster composed of participants and agents. The annotators considered them as ordinary drivers with medium scores. As for the previous cluster, the average behavior is reproduced in this situation.

Annotation cluster 4 is only composed of participants. It has some high scores on the specific memory and judgment scales. This behavior considered as slightly dangerous is also not reproduced by agents.

Discussion

In the logs cluster 1, the indicators were not able to distinguish the annotations cluster 1 from the rest of the main actors. The judgment scale is very high and a video replay shows that these participants tried to overtake several times unsuccessfully. Likewise, the participants of the annotations cluster 4 were not separated from logs cluster 2. This might be due to the fact that they dared to overtake just after the second oncoming vehicle, which requires to pull back in a short time frame. However, our indicators did not detect that difference.

Several behaviors can be annotated in the same way. This is an issue to analyze the similarity between the annotations clustering and the logs clustering with the RI measure. A solution could be compute the RI on logs clustering for which logs clusters annotated in the same way will be merged into one cluster.

We have a significant similarity between annotations and logs clusterings, meaning that we are able to classify our logs data into high-level behavior clusters which are meaningful in terms of driving annotations. Nevertheless the two clusterings are not identical with regard to the clusters composition. This could be due to the few number of annotators. This problem may also come from the clustering algorithm which is a classic but basic one. However, we already tried other algorithms such as EM and HAC without better results. We have to test with time-series based algorithms. Also, the experts of the domain have to be consulted to understand what missing indicator could catch these differences.

The third type of cluster (which does not appear in this experiment) is composed of agents only. In that case, we can consider -as no participant adopted this behavior -that the agents behavior is inaccurate (i.e. is an error) and should be investigated further. The method has to be applied in other situations in order to verify this particular case.

Conclusion & perspectives

This paper presents a method to study IVAs behaviors through an experiment in a VE. This validation is original in coupling an objective analysis of the agents behaviors through simulation logs, with a subjective analysis, coming from Human Sciences, of the situation-specific user categories through an annotation done by participants. The objective analysis uses an unsupervised clustering algorithm applied on simulation logs in order to classify participants behaviors, and an aggregation method to compare agents behaviors to humans ones. This comparison allows us to evaluate the agents behaviors credibility in terms of capacities, lacks, and errors. It also provides an analysis of which IVA parameter space produces which perceived behavior. The method is generic for VEs where agents aim at reproducing human behaviors. When applied to a new domain, some of the tools have to be adapted, such as the choice of the behavior questionnaire which is domain-specific.

Our validation method was applied to the road traffic simulation. This experiment showed that the methodology is usable for mixed and complex VEs and that it is possible to obtain high-level behaviors from the logs via our abstraction.

Several tracks for further work remain to explore. On the clustering part, the evaluation of multiple time series based algorithms should help classifying the temporal data. On the aggregation part, the automation of the parameter calibration will be beneficial to the agents aggregation accuracy.

Another research open issue is how the behaviors clustering evolve through multiple situations of a longer scenario, whether the participants clusters remain stable or change in number or composition.

Fig. 2 :

 2 Fig. 2: Logs preprocessing, clustering, and aggregation with the time (T ), the number of variables (X), of indicators (|inds|), and of main actors (|MA|).

Fig. 3 :

 3 Fig. 3: Application to the study of driving simulation


Marie Lange
email: m.lange@baclesse.fr

PhD Bénédicte Giffard
email: benedicte.giffard@unicaen.fr

MD Sabine Noal
email: s.noal@baclesse.fr

MD Olivier Rigal
email: olivier.rigal@chb.unicancer.fr

MD Jean-Emmanuel Kurtz
email: emmanuel.kurtz@chru-strasbourg.fr

PhD Natacha Heutte
email: n.heutte@baclesse.fr

MD Christelle Lévy

MD Djelila Allouache
email: d.allouache@baclesse.fr

Chantal Rieux
email: c.rieux@baclesse.fr

PhD Johan Le Fel
email: johan.lefel@chb.unicancer.fr

Aurélie Daireaux
email: aurelie.daireaux@neuf.fr

PharmD, PhD Bénédicte Clarisse
email: b.clarisse@baclesse.fr

MD Corinne Veyret
email: corinne.veyret@chb.unicancer.fr

MD Philippe Barthélémy
email: philippe.barthelemy@chru-strasbourg.fr

Nadine Longato
email: nadine.longato@chru-strasbourg.fr

PhD Francis Eustache

MD, PhD Florence Joly
email: f.joly@baclesse.fr

Jean-Emmanuel Pr

Kurtz

Dr Christelle Levy
email: c.levy@baclesse.fr

Francis Pr

Eustache

Pr Florence

Baseline cognitive functions among elderly patients with localized breast cancer

Keywords: Cognition disorders, breast neoplasms, ageing, neuropsychology, quality of life The

Purpose: Cognitive deficits (CD) are reported among cancer patients receiving chemotherapy, but may also be observed before treatment. Though elderly patients are expected to be more prone to present age-related CD, poor information is available regarding the impact of cancer and chemotherapy on this population. This study assessed baseline cognitive functions (before adjuvant treatment) in elderly early stage breast cancer (EBC) patients.

Methods: Women >65 years-old with newly diagnosed EBC were included in this prospective study. Episodic memory, working memory, executive functions and information processing speed were assessed by neuropsychological tests. Questionnaires were used to assess subjective CD, anxiety, depression, fatigue, quality of life, and geriatric profile. Objective CD were defined using International Cognition and Cancer Task Force criteria. A group of elderly women without cancer coupled with published data related to healthy women were used for comparison (respectively to subjective and objective CD).

Results: Among the 123 elderly EBC patients (70±4 years) included, 41% presented objective CD, which is greater than expected in healthy population norms (binomial test P<.0001).

Verbal episodic memory was mainly impaired (21% of patients). No correlation was observed between objective CD and cancer stage or geriatric assessment. Subjective CD only correlated with verbal episodic memory (P=.01).

Conclusions: This is the first large series assessing baseline cognitive functions in elderly EBC patients. More than 40% presented objective CD before any adjuvant therapy, which is a higher than what is reported among younger patients. Our results reinforce the hypothesis that age is a risk factor for CD in EBC patients.

This work was supported by a National grant (Programme Hospitalier de Recherche Clinique, PHRC) and Sanofi.

Introduction

Beyond difficulties with memory, attention and concentration reported by cancer patients, it has become increasingly apparent that cytotoxic drugs given for non-central nervous system tumours might induce cognitive side-effects. This phenomenon -called "chemobrain" -has been particularly studied among young women treated with chemotherapy for breast cancer.

According to the literature, these cognitive troubles could affect 15-50% of chemotherapytreated patients and are usually moderate in severity [START_REF] Joly | Cognitive dysfunction and cancer: which consequences in terms of disease management?[END_REF]. Nevertheless, recent longitudinal studies revealed that about 20 to 30% of breast cancer patients have cognitive impairment before starting adjuvant treatment [START_REF] Ahles | Brain vulnerability to chemotherapy toxicities[END_REF]. This indicates that beside exposure to cytotoxic drugs, other factors including postoperative dysfunctions, psychological distress related with the diagnosis, fatigue, genetic factors and also the biological adverse effects of cancer itself are involved, suggesting an impact of cancer as a whole on cognitive functions [START_REF] Scherling | Opening up the window into "chemobrain": a neuroimaging review[END_REF][START_REF] Ahles | Candidate mechanisms for chemotherapy-induced cognitive changes[END_REF].

The mean patients' age in the previous studies addressing the impact of cancer on cognitive function was less than 65 years. Yet, because cancer increasingly appears among seniors, the impact of ageing on cognitive impairment is a relevant issue. Ageing by itself is known to be associated with some cognitive modifications, comorbidities and functional decline, which may all have an impact on the patients' independence. While both ageing and cancer are expected to have an impact on cognition, biologic processes underlying cancer led to the hypothesis that age-associated declines among cancer patients would be parallel but higher to that of older adults with no cancer history, and that treatment-induced accelerated ageing would be observed only in vulnerable or frail populations [START_REF] Ahles | Brain vulnerability to chemotherapy toxicities[END_REF]. Although a pretreatment cognitive evaluation is a prerequisite to define the part of cognitive chemotherapy-induced impairment, only one study, to our knowledge, addressed this issue especially among elderly breast cancer patients [START_REF] Hurria | Cognitive function of older patients receiving adjuvant chemotherapy for breast cancer: a pilot prospective longitudinal study[END_REF].

The aim of the present prospective study was to precisely assess cognitive functioning (objective performances and subjective complaints) among elderly EBC patients before starting adjuvant therapy, and to seek for correlations with mood, fatigue, quality of life and clinical variables.

Patients and methods

Participants

Assessment

The neuropsychological battery included standardized neuropsychological tests assessing 4 cognitive domains: episodic memory (verbal and visual modalities), working memory, information processing speed, and executive functions (Table 1) [START_REF] Kalafat | French standardization of the "Mini Mental State" (MMS) GRECO version[END_REF][START_REF] Cardebat | Formal and semantic lexical evocation in normal subjects. Performance and dynamics of production as a function of sex, age and educational level][END_REF][START_REF] Rey | Manual of copy and memory reproduction test of complexe geometric figures[END_REF][START_REF] Reitan | Validity of trail making tests as an indicator of organic brain damage[END_REF][START_REF] Van Der Linden | Assessment of memory impairment[END_REF][START_REF] Wechsler | Wechsler Adult Intelligence Scale-III[END_REF]. The subjective assessment consisted of a self-report measure of cognitive complaints (Functional Assessment of Cancer Therapy Cognitive Scale -FACT-Cog, version 3 [START_REF] Joly | French version of the Functional Assessment of Cancer Therapy-Cognitive Function (FACT-Cog) version 3[END_REF][START_REF] Wagner | Measuring Patient Self-Reported Cognitive Function: Development of the Functional Assessment of Cancer Therapy-Cognitive Function Instrument[END_REF] [START_REF] Spielberger | Manual for the State-Trait Anxiety Inventory (STAI)[END_REF]), fatigue (Functional Assessment of Chronic Illness Therapy-Fatigue -FACIT-Fatigue, version 4 [START_REF] Yellen | Measuring fatigue and other anemia-related symptoms with the Functional Assessment of Cancer Therapy (FACT) measurement system[END_REF]) and quality of life (FACT-Breast -FACT-B, version 4 [START_REF] Brady | Reliability and validity of the Functional Assessment of Cancer Therapy-Breast quality-of-life instrument[END_REF] incorporates FACT -General -FACT-G, version 4 [START_REF] Cella | The Functional Assessment of Cancer Therapy scale: development and validation of the general measure[END_REF]). Geriatric assessment included the Geriatric Depression Scale (GDS) [START_REF] Clement | Development and contribution to the validation of a brief French version of the Yesavage Geriatric Depression Scale[END_REF] (4item short form), the Instrumental Activities of Daily Living (IADL) [START_REF] Lawton | Assessment of older people: self-maintaining and instrumental activities of daily living[END_REF], the Activities of Daily Living (ADL) [START_REF] Katz | Progress in development of the index of ADL[END_REF], the Charlson comorbidity index [START_REF] Charlson | A new method of classifying prognostic comorbidity in longitudinal studies: development and validation[END_REF], the number of medications and main previous medical history. Clinical variables were Performance Status (PS), medications with potential impact on cognition (Level 3 on the WHO analgesic ladder, anxiolytics, antidepressant treatments and hypnotics), cancer stage, time since surgery, type of surgery, HER2 positive, and hormone receptors status.

Procedure

Patients completed neuropsychological tests, geriatric scales, and some self-report questionnaires (the BDI and the STAI) in a 2 hour-session with a neuropsychologist. The other self-report questionnaires were completed by the patients at home.

Assessment criteria

According to the recommendations of the International Cognition and Cancer Task Force [START_REF] Wefel | International Cognition and Cancer Task Force recommendations to harmonise studies of cognitive function in patients with cancer[END_REF] and as described previously [START_REF] Wefel | Cognitive impairment in men with testicular cancer prior to adjuvant therapy[END_REF], an index for each patient's baseline overall cognitive function was operationally defined as impaired or not impaired using a 2-part criterion: if patients performed at a z-score of ≤1.5 standard deviation (SD) on 2 or more tests, or if they performed at a z-score of ≤2.0 SDs on a single test, they were classified as impaired. This 2step approach was designed to minimize the number of potential false-positive errors resulting from multiple tests and to determine the frequency of impairment rather than low performance. By using curves based on the binomial probability distribution [START_REF] Ingraham | An Empirical Approach to Determining Criteria for Abnormality in Test Batteries With Multiple Measures[END_REF], we determined that in a battery of 8 independent tests approximately 17% of the population would perform 2 SDs below the mean on a single test, making 17% considered as the significant threshold. Like for overall cognitive function, using the classification criteria described above, a cognitive domain was considered as impaired if it included one impaired score.

As a reference of self-report cognitive complaints in the general population, a group of 71 healthy women matched on age and education to the EBC patients included (recruited through local advertisements and among associations) completed the FACT-Cog. Clinically significant symptoms of mood disorders and fatigue were operationally defined as ratings on the BDI ≥8 [START_REF] Beck | Assessment of depression: the depression inventory[END_REF], STAI ≥56 [START_REF] Spielberger | Manual for the State-Trait Anxiety Inventory (STAI)[END_REF] and FACIT-Fatigue <37 [START_REF] Wratten | Fatigue during breast radiotherapy and its relationship to biological factors[END_REF]. Geriatric profile was established using GDS (0-4, high score = more depression), IADL (0-8, low score = no functional status problem) and ADL (0-6, high score = no functional status problem) scores; patients were considered as having a frailty profile if they had at least one alteration of these scores (GDS>0, IADL>0, ADL<6).

Statistical Analysis

Published normative data, adjusted for age and/or education, were used to convert patients' raw neuropsychological tests scores into standardized scores (z scores; mean, 0; SD, 1).

Descriptive statistics were generated for the socio-demographic and clinical variables.

Comparisons were made by chi square, Student's, and Wilcoxon's tests. The correlations between cognitive complaints and objective cognitive scores and other self-report measures were assessed with Spearman's rank correlation coefficient. Given the large number of correlations performed, a p-value <0.01 was considered in order to minimize type I error. All analyses were conducted using SAS version 9.3.

Results

Sample characteristics

Of 221 elderly patients with EBC screened, 11 were ineligible, and 82 were not enrolled in the trial for the following reasons: lack of interest (n=17), too much burden (n=9), travel limitations (n=17), duration of the assessment (n=10), or other reason (n=29). This yielded a 61% participation rate. Moreover, 5 patients were excluded from analysis because of a score above the threshold of dementia [START_REF] Folstein | Mini-mental state". A practical method for grading the cognitive state of patients for the clinician[END_REF]. Hence, the final sample consisted of 123 patients, whose major characteristics are presented in Table 2. The majority of elderly patients did not exhibit geriatric comorbidities, was healthy (PS 0=91%) and two thirds had a low level of education.

Neuropsychological outcomes

Using the classification criteria described above, 41% of patients (51/123, binomial test P<.0001) had impaired overall cognitive function which is significantly more frequent than what would be expected in the general population. Twenty nine percent (36/123) exhibited impairment on 1 test, whereas 12% (15/123) exhibited impairment on 2 or more tests. Main impairment was related to visual episodic memory and executive functions (21 and 16% of patients, respectively -cf. Fig. 1). Raw neuropsychological test scores, z-scores or standard scores are shown in Table 3.

Cognitive complaints

Healthy subjects had significantly more complaints on Perceived Cognitive Impairments and Perceived Cognitive Abilities FACT-Cog subscales than patients (Fig. 2). However, patients had more complaints than healthy subjects on the subscale Impact on Quality of Life of cognitive impairment (P<.025).

Anxiety, depression, fatigue, geriatric and quality of life scores

Anxiety, depression and severe fatigue were observed in 6%, 10% and 29% of the patients, respectively. Regarding geriatric scores, 89% and 87% of the patients had normal GDS and functional status scores. Quality of life scores are shown in Table 3.

Relation between cognitive complaints and neuropsychological scores, anxiety, depression and fatigue

As shown in Table 4, 4). Furthermore, the same 3 subscales were overall significantly associated to quality of life scores (FACT-B and FACT-G; Table 4).

Neuropsychological outcomes and clinical characteristics

Cognitive impairment was not significantly correlated with geriatric profile (P=0.83).

Furthermore, there was no correlation between cognitive impairment and Charlson index, PS, medications with potential impact on cognition, cancer stage, time since surgery, type of surgery, hormonal receptor and Her2 status.

Discussion

This study is the first large series assessing cognitive functions in elderly EBC patients prior to adjuvant treatment. The main result is that, compared to normative data based on age and education, 41% of the patients had cognitive impairment mainly epitomized by impaired visual episodic memory before any adjuvant treatment which is significantly higher than what would be expected considering healthy population norms. To avoid confusing bias from pretreatment status to that of chemotherapy-induced impairment, some authors proposed the term "chemobrain" to be replaced by "cancerbrain" [START_REF] Hede | Chemobrain is real but may need new name[END_REF][START_REF] Hurria | Renaming "chemobrain[END_REF]. In this way, evaluation of cognitive functions before treatment appears essential to understand the impact of treatments on cognitive functions of cancer patients especially among elderly patients.

In our study, the proportion of patients exhibiting pre-treatment impairments was higher than that the one reported in studies focusing on younger breast cancer patients [45-55 years-old] ranging from 20-30% [START_REF] Ahles | Brain vulnerability to chemotherapy toxicities[END_REF]. This important finding supports the hypothesis that elderly patients may be more sensitive to the impact of cancer on cognition, and would be consistent with the link between biological processes underlying cancer, ageing, neurodegeneration, and a cognitive decline as proposed by T.A. Ahles [START_REF] Ahles | Cancer-and cancer treatment-associated cognitive change: an update on the state of the science[END_REF]. However, longitudinal studies remains necessary to investigate whether or not cancer therapies accelerate cognitive ageing [START_REF] Ahles | Cancer-and cancer treatment-associated cognitive change: an update on the state of the science[END_REF].

In a previous pilot longitudinal study exploring baseline cognitive functioning of elderly patients with breast cancer (n = 28), 11% of the patients were found to have cognitive impairment before beginning chemotherapy [START_REF] Hurria | Cognitive function of older patients receiving adjuvant chemotherapy for breast cancer: a pilot prospective longitudinal study[END_REF]. However, 86% of the patients had an education level superior to high school (against only 18% in our population), which may have biased the results and underestimated the impact on cognition. Thus, according to the concept of cognitive reserve suggesting that some subjects may cope better than others with brain damage, high education level could reduce the sensitivity of patients to the impact of cancer on cognition [START_REF] Ahles | Brain vulnerability to chemotherapy toxicities[END_REF].

To our knowledge, the present study is unprecedented in exploring differences between elderly breast cancer patients and healthy controls with the FACT-Cog. For 2 of the 4 FACT-Cog subscales, breast cancer patients reported significantly less cognitive complaints (Perceived Cognitive Impairments and Perceived Cognitive Abilities) than healthy controls, but seemed to have more complaints on Impact on Quality of life subscale. One hypothesis could be that patients with breast cancer are more likely to put cognitive impairment into perspective due to the context of the disease, even though those minor difficulties may indeed have an impact on their quality of life significantly.

The present data also suggest that, in accordance with previous studies, cognitive complaint scores were correlated with anxiety, depression and fatigue scores. However, no correlation was found with overall objective cognitive scores [START_REF] Pullens | Subjective cognitive dysfunction in breast cancer patients: a systematic review[END_REF], except between verbal episodic memory impairment and the Perceived Cognitive Impairments subscale. The latter could allow assessing this cognitive domain, especially regarding memory verbal information retrieval (accounting for one third of this subscale items). These results are consistent with those reported by Ganz and colleagues based on cognitive complaint questionnaire assessing 4 subscales specific to one cognitive domain, which suggested that subjective cognitive complaints partly reflect objective performance in domain-specific cognitive test [START_REF] Ganz | Cognitive complaints after breast cancer treatments: examining the relationship with neuropsychological test performance[END_REF].

In the present study, all patients underwent a selected geriatric assessment. Interestingly, the results showed that the presence of at least one impaired cognitive domain was not significantly correlated with geriatric fragility assessed from GDS and functional status (ADL and IADL). However, the definition of geriatric fragility could be considered as relatively strict (at least one score of the 3 scales altered), and only a few number of patients were considered as having a geriatric frail profile, which could represent a selection bias of our sample. Furthermore, the large majority of the patients included in our study were in relatively good general health as indicated by the proportion with PS 0 (91%) or Charlson index 0 (78%), suggesting that the proportion of patients with cognitive impairments (40%) could have been underestimated. Indeed, nobody can exclude that the proportion of cancer patients with cognitive impairment prior to adjuvant treatment could be higher in a global and more heterogeneous population regarding geriatric conditions.

While the present quality of life-related data appeared to be in the same range as those reported in the overall cancer population [START_REF] Brucker | General population and cancer patient norms for the Functional Assessment of Cancer Therapy-General (FACT-G)[END_REF], cognitive functioning in elderly patients remains an important issue to be taken into account in the decision making of adjuvant treatment.

The lack of direct comparison with a group of healthy subjects remains the main limitation of this study. Furthermore, the population of patients was clinically heterogeneous regarding some characteristics (cancer stage, hormonal receptor status, type of surgery…). Another possible selection bias could be the low number of geriatric scores in our population or the impact of patients' motivation.

In conclusion, this study is the first large series assessing baseline cognitive functions in elderly EBC patients. The main finding is that cognitive impairment prior to adjuvant therapy was more frequent than what is observed or reported in both comparative healthy elderly subjects or younger breast cancer patients; this reinforces the hypothesis that age-associated decline among cancer patients is pronounced (i.e., age is a risk factor for CD in breast cancer patients). Evaluation of cognition before treatment is essential to take into account the impact of treatments on cognitive functions, especially among elderly cancer patients. Furthermore, cancer treatments could accelerate the ageing process in a vulnerable or frail population. In this respect, additional research including such baseline assessment is needed to understand, anticipate and manage the short-and long-term effects of cancer therapy on the cognitive function of elderly patients. There were significant differences between patients and healthy subjects on 2 FACT-Cog subscales. The subscale Comments from Others was not represented because there was no difference between groups.

  Newly diagnosed and consecutive elderly women with EBC were recruited from 3 French Cancer comprehensive Centers (Caen, Rouen, and Strasbourg) from January 2009 to August 2012. Inclusion criteria were EBC and age over 65. Exclusion criteria included prior exposure to chemotherapy or radiotherapy, neurological comorbidities, known psychiatric comorbidities which might affect capacity to participate, major cognitive disorders and documented alcohol or drug abuse. Participants with a Mini-Mental State Examination (MMSE) score less than 25 out of 30 suggesting potential pathological ageing were not eligible [6-8] as well as those reporting a formal education less than 5 years (end of the primary school) due to the lack of normative data for such individuals. Patients were assessed after surgery, but before any adjuvant treatment initiation. They were evaluated with standardized neuropsychological tests, by a graduate neuropsychologist, and through self-report questionnaires. Cognitive performances were compared to published normative data, adjusted for age and/or education. All patients gave their written informed consent to the longitudinal study which was approved by the local ethics committee.

  cognitive complaints were correlated with verbal episodic memory impairment (Perceived Cognitive Impairments subscale, P<.01) but overall cognitive scores were not correlated with cognitive complaints (the 4 subscales of the FACT-Cog). However, 3 of the 4 subscales of the FACT-Cog (Perceived Cognitive Impairments, Perceived Cognitive Abilities and Impact on Quality of Life) were significantly correlated with the measures of depression, anxiety, and fatigue (P<.001; Table

FR: free

  recall, DFR: delayed free recall, FACT-G subscales: PWB: Physical Well-Being, SWB: Social/Family Well-Being, EWB: Emotional Well-Being, FWB: Funcional Well-Being. PCI: Perceived Cognitive Impairment, PCA: Perceived cognitive abilities, QoL: Impact on Quality of Life, Oth: Comments from others. *p<0.01, ** p<0.001

Figure 1 .Figure 1 .

 11 Figure 1. Distribution of patients according to impaired cognitive domain

Figure 2 .

 2 Figure 2. Median FACT-Cog scores of patients and healthy subjects.

Table 1 .

 1 Neuropsychological tests grouped by main cognitive domains

	Cognitive domain	Test	Outcome measure	Range
	Episodic memory
	Verbal episodic	Grober and Buschke	4 free recalls	(4x) 0-16
	memory	procedure 20
	Visual episodic	Rey Complex Figure 19	Recall score	0-36
	memory
	Working memory	WAIS-III 21 : Arithmetic	Number of resolved	0-22
			problems
		WAIS-III: Digit-span	Correct trials, forward	0-16
			Correct trials, backward	0-14
		WAIS-III: Letter-number	Total correct trials	0-21
		sequencing
	Information	TMT A 18	Time to complete and	≥0
	processing speed		errors
	Executive function Flexibility	TMT B 18	Time to complete and	≥0
			number of perseverative
			errors
	Information	Verbal fluency 17 : Category	Total score over 2 min	≥0
	generation	(animal) and Letter P
	WAIS: Wechsler Adult Intelligence Scale, TMT: Trail Making test

Table 2 .

 2 Demographic and clinical characteristics of all patients (n=123)

	Demographic
	Age (years) (mean, SD, range)	70 (4.10) [65-83]
	Education level (low / middle / high) (%)	66 / 15 / 18
	(mean, SD)	11 (2.77)
	Clinical
	PS (WHO=0) (%)	91
	Co-morbidities (%) Charlson index (0 / 1-2)	78 / 22
	> 3 co-medications	27
	Medications with potential impact on cognition* (%)	24
	Cancer stage I-II (%)	87
	Time since surgery (days) (median, range)	36 [19-141]
	Lumpectomy / Mastectomy (%)	72 / 28
	Lymph node dissection (%)	80
	HER2 positive (%)	17
	Hormone receptors positive (%)	88
	SD: standard deviation, PS: Performance Status, WHO: World Health Organization
	* Level 3 on the WHO analgesic ladder, anxiolytics, antidepressant treatments and hypnotics

Table 3 .

 3 Neuropsychological test and quality of life outcomes FR: free recall, DFR: delayed free recall, TMT: Trail Making test, PWB: Physical Well-Being, SWB: Social/Family Well-Being, EWB: Emotional Well-Being, FWB: Functional Well-Being, PCI: Perceived Cognitive Impairment, QoL: Impact on Quality of Life, Oth: Comments from others, PCA: Perceived cognitive abilities

	Cognitive scores	No.	Mean SD	z scores or
					standard scores
	Episodic memory
	FR1
	FR2
	FR3
	DFR
	Rey recall
			27.75	7.03	0.10 (0.88)
			19.24	6.50	0.03 (1.03)
			111.43	45.26	-0.40 (0.67)
			0.52	0.85	0.42 (1.18)
	Quality of life scores
	FACIT-Fatigue	111	40.02 9.16
	FACT-B	112	23.40 6.27
	FACT-G global score	110	81.83	10.56
	PWB	110	24.54	3.31
	SWB	111	19.97	4.15
	EWB	112	19.19	3.62
	FWB	112	17.96	4.35
	FACT-Cog
	PCI	112	60.16	9.91
	QoL	106	11.63	4.06
	Oth	110	15.55	1.11
	PCA	108	19.38	4.97
	No.: number,

Table 4 .

 4 Relations between cognitive complaints and neuropsychological, anxiety, depression, fatigue and quality of life scores

	Cognitive complaints

Acknowledgments:

This work was supported by a National grant (Programme Hospitalier de Recherche Clinique, PHRC) and Sanofi.

Disclosures: Financial disclosures: Sanofi.

QoL: Quality of Life