Mehdi Rahim
email: mehdi.rahim@cea.fr

Bertrand Thirion

Claude Comtat

Gaël Varoquaux

* -M Rahim

Transmodal Learning of Functional Networks for Alzheimer's Disease Prediction

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Introduction

Brain imaging can probe the signatures, anatomical or functional, of brain diseases. For Alzheimer's Disease (AD), anatomical measurements on Magnetic Resonance Images (MRIs) such as the hippocampus volume [START_REF] Cuingnet | Automatic classification of patients with Alzheimer's disease from structural MRI: a comparison of ten methods using the ADNI database[END_REF] or the cortical thickness [START_REF] Sabuncu | The dynamics of cortical and hippocampal atrophy in Alzheimer disease[END_REF][START_REF] Prados | Measuring brain atrophy with a generalised formulation of the boundary shift integral[END_REF] are accurate biomarkers that help to distinguish AD subjects from Mild Cognitive Impairment (MCI) subjects who convert later to AD [START_REF] Trzepacz | Comparison of neuroimaging modalities for the prediction of conversion from mild cognitive impairment to alzheimer's dementia[END_REF].

Another imaging modality, Positron Emission Tomography (PET), measures brain biochemical and functional dynamics according to specific radiotracers. For example, the Pittsburgh compound B (PiB) radiotracer quantifies beta-amyloid plaques deposition that is at the root of AD [START_REF] Villain | Regional dynamics of amyloid-β deposition in healthy elderly, mild cognitive impairment and Alzheimer's disease:A voxelwise PiB-PET longitudinal study[END_REF]. FDG-PET (fluorodeoxyglucose PET) measures quantitatively brain metabolic activity and is considered as sufficiently accurate biomarker of AD. FDG-PET analysis has shown that a specific pattern of decrease of metabolic activity characterizes AD subjects with an accuracy of 90% [START_REF] Gray | Multi-region analysis of longitudinal FDG-PET for the classification of Alzheimer's disease[END_REF][START_REF] Toussaint | Resting state FDG-PET functional connectivity as an early biomarker of Alzheimer's disease using conjoint univariate and independent component[END_REF] However, PET imaging is costly and implies exposing the subject to radiations.

More recently, studies have shown promising use of resting-state functional MRI (rs-fMRI) as a biomarker of AD [START_REF] Castellanos | Clinical applications of the functional connectome[END_REF], [START_REF] Dennis | Functional brain connectivity using fMRI in aging and Alzheimer's disease[END_REF]. Rs-fMRI is an easy-toimplement imaging protocol. It reveals functional interactions between brain networks that predict brain states [START_REF] Richiardi | Decoding brain states from fMRI connectivity graphs[END_REF], via the intrinsic functional connectivity (FC) estimated from the blood-oxygen-level dependent (BOLD) signal. As studied in [START_REF] Wang | Altered functional connectivity in early Alzheimer's disease: A restingstate fmri study[END_REF], [START_REF] Greicius | Default-mode network activity distinguishes Alzheimer's disease from healthy aging:evidence from functional MRI[END_REF] and [START_REF] Koch | Diagnostic power of default mode network resting state fMRI in the detection of Alzheimer's disease[END_REF], AD is characterized by widespread decreases in connectivity, especially in the default mode network (DMN). Although functional connectivity brings a supplementary information on AD by mapping functional regions that share some common dynamics, its sensitivity to classify correctly AD subjects [START_REF] Jiang | Intrinsic functional component analysis via sparse representation on Alzheimer's disease neuroimaging initiative database[END_REF] is lower than the anatomical MRI features and FDG-PET. fMRI has a low signal-to-noise ratio (SNR), measures of functional connectivity are often limited by the quantity and quality of data. Reproducibility of the connectome is another major limitation. Indeed, many studies ( [START_REF] Choe | Reproducibility and temporal structure in weekly resting-state fMRI over a period of 3.5 years[END_REF], [START_REF] Tomasi | Resting functional connectivity of language networks: characterization and reproducibility[END_REF]) have shown that good reproducibility is achievable only with longer time-series and after several re-test sessions.

Recent studies correlating FDG-PET to rs-fMRI have shown that functional connectivity at a regional level reflects the metabolic activity. In [START_REF] Riedl | Local activity determines functional connectivity in the resting human brain: a simultaneous FDG-PET/fMRI study[END_REF], the authors have presented a simultaneous PET/MR study on subjects watching visual stimuli. Correlations were found between metabolism and seed-based functional connectivity in visual areas during stimulation. Other works [START_REF] Passow | Default-mode network functional connectivity is closely related to metabolic activity[END_REF][START_REF] Aiello | Relationship between simultaneously acquired resting-state regional cerebral glucose metabolism and functional MRI:A PET/MR hybrid scanner study[END_REF] have shown significant correlation between the metabolism and the functional connectivity at the DMN level, in particular in the precuneus and the posterior cingulate cortex. Our contribution builds on this relationship between metabolism and connectivity: we conjecture that patterns specific to AD in fMRI and PET are equivalent [START_REF] Gusnard | Searching for a baseline:functional imaging and the resting human brain[END_REF]. We propose a transmodal learning framework that estimates a predictive model classifying AD from rs-fMRI -noninvasive but weakly accurate-informed by an FDG-PET (invasive but accurate) discriminative pattern. Unlike multi-modal learning that combines different modalities observed in each subject, transmodal learning relies on a metabolic model to inform a connectivity model. This transfer implies that our connectivity-based model can be used on rs-fMRI data acquired independently from the FDG-PET dataset used to build the metabolic prior. As there have already been many studies of AD with FDG-PET, a lot of information can be leveraged to improve connectivity-based prediction. Our goal is to have a more accurate discriminant model on non-invasive rs-fMRI to avoid as much as possible the use of an invasive modality. The proposed model addresses the issue of the arbitrary selection of a reference region of interest (ROI) when computing functional connectivity. Seed-based correlations are computed according to ROIs extracted from a functional atlas, followed by a model that stacks their predictions. Such an atlas may include ROIs that do not exhibit high correlation between connectivity and metabolism. However, the stacking model that combines them will select the most relevant ROIs. Resulting predictor, based on multiple ROIs, gives better accuracy than a single ROI.

Prior work in the neuroimaging literature has relied on informing a given imaging modality with a prior extracted from a different but related modality. In particular, potential correlations between functional and anatomical characteristics have been considered. In [START_REF] Ng | A novel sparse graphical approach for multimodal brain connectivity inference[END_REF], functional connectivity matrix was learned from rs-fMRI with a structural-connectivity constraint from diffusion weighted MRI (dMRI). [START_REF] Jiang | Fiber connection pattern-guided structured sparse representation of whole-brain fMRI signals for functional network inference[END_REF] proposed a unsupervised decomposition of resting-state time-series from rs-fMRI. This decomposition is constrained by a fiber connection pattern extracted from dMRI. While [START_REF] Venkataraman | Joint modeling of anatomical and functional connectivity for population studies[END_REF] introduced a joint functional/structural connectivity estimation that helped to identify joint connectivity variations in schizophrenia. However, these two modalities should be combined with caution. Indeed, [START_REF] Ng | Implications of inconsistencies between fmri and dmri on multimodal connectivity estimation[END_REF] highlighted possible inconsistencies between fMRI and dMRI. From a methodological standpoint, our work is related to transfer learning methods [START_REF] Pan | A survey on transfer learning[END_REF] that apply a model learned from a dataset (or modality) A to a dataset B. In AD classification, [START_REF] Cheng | Domain transfer learning for MCI conversion prediction[END_REF] has used a transfer-learning scheme called auxilary model to enhance mildcognitive-converters prediction from FDG-PET, anatomical MRI, and protein biomarkers. The main novelty of our approach is that we use the learned model as a constraint to the classification training.

A preliminary work has been presented in [START_REF] Rahim | Integrating multimodal priors in predictive models for the functional characterization of alzheimer's disease[END_REF], where we have shown the feasibility of the approach on a smaller dataset. Here, after detailing the transmodal learning model, we extensively validate and analyze it: i) we compare this approach to classical approaches; ii) we assess the impact of parameter choice; iii) we extend the approach to a multi-modal dataset, where we show that the metabolism-informed connectivity yields more accurate classification of AD.

In section 2, we detail the transmodal learning framework for the connectivitybased prediction. Then we present in section 3 data used in our experiments that have been extracted from 211 subjects across 694 fMRI scans of the ADNI database. The results shown in section 4 demonstrate the efficiency of the approach compared to classical connectivity-based classification.

Methods

Here we introduce an enhanced predictive model of AD from functional connectivity computed from resting-state fMRI. This model exploits accurate and discriminative knowledge from previous FDG-PET studies by assuming that changes in metabolic activity yield information comparable to functional connectivity alterations.

We propose a voxel-based classifier that handles the connectivity-metabolism relationship. For this purpose, region-to-voxel connectivities are computed according to several regions of interest (ROIs). This leads to one predictive model per ROI, hence these models have to be combined at a later stage. This problem is often depicted as a multi-source learning and several solutions have been proposed such as multiple kernel learning (MKL) [START_REF] Rakotomamonjy | Simplemkl[END_REF]. In the MKL setting, a kernel is computed for each source. Then a linear combination of the kernels is learned to optimize the classification of the sources. Another solution is to train a classifier on each source, and then to use majority voting to determine the final class assignment. We use a stacking approach, relying on a second-stage classification model that takes as input model predictions from each ROI. This approach makes no assumptions on

Voxel-based classification

Feature extraction Classification

Regions of interest Atlas ...

FDG-PET dataset

Subject 1 Subject Npet Subject 1

rs-fMRI dataset ...

Subject Ns

Figure 1: Overview of the proposed connectivity classification framework. First, region-to-voxel connectivity is computed from rs-fMRI timeseries according to each ROI of a brain atlas. This yields N r connectivity maps for each subject. The FDG-PET prior is a vector of weights learned from a classification model on a distinct and larger dataset than the rs-fMRI dataset. These weights are integrated to regularize a linear classifier on the ROIs connectivity maps. Finally, predictions of all ROIs are concatenated in a matrix which is given as input to a stacking model to predict the clinical group.

the classifiers that should be used, and is well suited to heterogeneous data. It can also be used to recover the discriminant features and spatial maps, as it operates at the voxel-level with a linear classifier.

Transmodal Learning Framework Overview

The proposed pipeline is depicted in Fig. 1. The inputs are 4D acquisitions of resting-state fMRI, and a set of regions of interest (ROIs) from which the connectivity is estimated. First, the prior is estimated from 3D FDG-PET images of metabolism at the voxel-level with a linear classifier. The restingstate fMRI features are obtained by computing region-to-voxel connectivity maps from fMRI data. Region-to-voxel connectivity maps depend strongly on the the specific choice of seed region [START_REF] Cole | Advances and pitfalls in the analysis and interpretation of resting-state FMRI data[END_REF]. To avoid relying on a single ROI, we propose to combine a set of ROIs extracted from a brain atlas. Our goal is to aggregate discriminative functional-connectivity features from an atlas covering various functional networks. We will discuss in section 4.4 which atlas should be selected. Then, an enhanced regression model informed by the FDG-PET prior is estimated; this yields one model per ROI. Finally, the global fMRI-based classification is performed via another classifier that estimates the target from the stacked predictions of each ROI-based model. N v number of voxels of the entire brain. N r number of ROIs. N s number of subjects of the rs-fMRI dataset.

A

brain atlas: set of N r brain regions r i , such that:

A = { r i } with 1 ≤ i ≤ N r . ts (s k ) v j time-series at a voxel v j in subject s k . ts (s k ) r i averaged time-series across all voxels of ROI r i in subject s k . f c (s k ) r i ,v j connectivity at voxel v j according to ROI r i in subject s k . f c (s k ) r i 1 ,r i 2 connectivity between ROI r i 1 and ROI r i 2 in subject s k . fc (s k ) r i connectivity map (N v voxels) in subject s k according to ROI r i . X r i
connectivities of all subjects according to ROI r i , its dimension is (N s , N v ). y subject binary class (diagnosis) vector, its dimension is N s . N spet number of subjects of the FDG-PET dataset.

X pet feature matrix of the FDG-PET dataset, its dimension is (N spet , N v ). y pet diagnosis vector of dimension N spet .

w prior linear classifier coefficients of the FDG-PET dataset, its dimension is N v .

w r i linear classifier coefficients from the connectivity values associated with the seed region r i .

Table 1 gives the notations that we use for the mathematical formulations.

Functional Connectivity Features

There are generally two ways to compute FC:

Region-to-voxel Region-to-voxel connectivity measures correlations from a given seed ROI to the whole brain. It gives for each ROI a connectivity map at the voxel level. Choosing a seed ROI is difficult, as it changes vastly this connectivity map. We overcome this limitation by considering a set of ROIs. Typically, each region of a given brain atlas A is used as a seed ROI for a connectivity map. For a subject s k , we define the region-to-voxel connectivity f c (s k ) between any voxel and the region of interest r i as the Pearson correlation between their time-series [START_REF] Zalesky | On the use of correlation as a measure of network connectivity[END_REF]. The correlation values are then normalized using Fisher's Z-transformation. It converts correlations to normally distributed values for a better comparison.

Region-to-region Region-to-region connectivity f c

(s k )
r i 1 ,r i 2 between ROI r i 1 and ROI r i 2 is the correlation between their respective averaged timeseries. The correlation is also normalized with Fisher's Z-transformation. This measurement on several ROIs (or atlas) yields a connectivity matrix (connectome) characterizing a subject [START_REF] Varoquaux | Learning and comparing functional connectomes across subjects[END_REF]. It is not used in our approach, which relies on only region-to-voxel connectivities, but we will compare the two sets of features in the experiments.

The connectivity map fc (s k ) associated with a ROI r i is a vector of dimension N v that represents the correlations of all brain voxels

fc (s k ) r i = f c (s k ) r i ,v 1 , ... , f c (s k ) r i ,v j , ... , f c (s k ) r i ,v Nv . (1)
We define the feature matrix X r i of dimension (N s , N v ) associated with ROI r i as:

X r i = fc (s 1 ) r i , ... , fc (s k ) r i , ... , fc (s Ns ) r i . (2)
This matrix will be used for the diagnosis prediction. We compute one classification model per ROI. Then the diagnosis is learned from the models predictions according to all ROIs.

Data-Driven Metabolic Prior Integration

Rather than a multi-modal PET-fMRI prediction in each subject, we derive independently a population-level PET prior, thus avoiding the additional requirement of one PET acquisition per subject for diagnosis. The estimation of a connectivity-based classification model is regularized by a learned metabolic prior, which involves a complementary coupling parameter to adapt this prior.

Prior Estimation

The metabolic prior is composed of discriminative coefficients of a linear model learned from N spet FDG-PET images of a dataset distinct from the rs-fMRI dataset. It is the core of the transmodal approach that we propose. We define a feature matrix X pet representing the metabolism in each voxel and a label vector y pet representing the target (diagnosis). A linear model is calculated on this matrix X pet , where the model coefficients ŵprior are estimated as follows:

ŵprior = argmin w∈R Nv L(X pet w, y pet ) + Ω(w), ( 3
)
where L is the loss function of the prediction which can be seen as a datafidelity term. Ω is a regularization term. We keep this general formulation of a linear classifier since we are free to choose which classifier to use. In out experiments, we use a 2 regularized logistic regression classifier to generate the metabolic prior w prior ∈ R Nv . It will be integrated into the functional connectivity classification.

Transmodal Classification

The integration of the metabolic prior into functional connectivity-based classification is done at the voxel level, by assuming that the connectivity features and the metabolic prior are estimated in the same spatial referential. The prior coefficients w prior act as regularizers of a linear model on the functional connectivity. The model operates on the connectivity features X r i computed from [START_REF] Sabuncu | The dynamics of cortical and hippocampal atrophy in Alzheimer disease[END_REF]. It integrates the prior within the penalization term yielding

ŵ r i = argmin w∈R Nv X r i w -y 2 2 + α w -λw prior 2 2 , (4)
where w prior is the vector of weights that has already been learned. α > 0 is a penalization parameter that controls the amount of shrinkage. We use the least-squares loss function so that the regularization can be easily integrated.

By substituting b = w -λw prior , one falls back to a classical ridge regression formulation. λ > 0 is a scaling parameter that adapts the prior to the actual setting. Here we assume that the discriminative weights of the metabolism and the functional connectivity are linearly correlated. When λ is zero, there is no effect of the prior, hence the weights estimated depends solely on connectivity features. Having λ too large imposes the rs-fMRI model to fully replicate the FDG-PET model, in effect underfitting the fMRI data. Model parameters λ and α are empirically estimated through a nested crossvalidation on the training set. The resulting ŵ r i ∈ R Nv is the PET-informed coefficient vector of the classifier according to ROI r i .

Stacking Connectivity-Based Predictions

We introduce here the stacking model to predict the diagnosis by learning from the predictions of the atlas ROIs. The unthresholded predictions from (4) of all ROIs are concatenated, which yields the following matrix:

S ∈ R Ns×Nr S = X r 1 ŵr 1 , ... , X r i ŵr i , ... , X r Nr ŵr Nr , (5)
where each column i represents the predictions of ROI r i in all subjects. The subject class is learned from another classifier on the matrix S, such as a logistic regression classifier:

ŵs =argmin w∈R Nr , c∈R C 2 w T w + Ns k=1 log(exp(-y k (S (k) w + c)) + 1). ( 6
)
where C controls the regularization, c is the intercept and ŵs are the estimated stacking model coefficients to define the final discriminative model. Since the stacking is actually compatible with any kind of classifier, nonlinear classifiers (e.g. random forests) can be used as well. We discuss the choice of the classifier further by considering their impact on the model accuracies. We discuss also the impact of taking different sets of ROIs from different functional atlases.

3 Data and Experiments

ADNI Dataset

The data used in the preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database adni.loni. usc.edu. The ADNI was launched in 2003 as a public-private partnership, led by Principal Investigator Michael W. Weiner, MD. The primary goal of ADNI has been to test whether serial magnetic resonance imaging (MRI), positron emission tomography (PET), other biological markers, together with clinical and neuropsychological assessment, can be combined to measure the progression of mild cognitive impairment (MCI) and early Alzheimer's disease (AD). For up-to-date information, see www.adni-info.org.

Subject Information

The subjects selected from the ADNI database in our study belong to two datasets (one dataset per modality). Let us emphasize that the PET dataset is different from the fMRI dataset; our goal is precisely to assess how metabolism measurements can inform the connectivity without resorting to a multi-modal PET-fMRI prediction in each subject. The PET dataset is used to build the metabolic prior. For this purpose, we select PET scans of 1371 subjects at baseline. Concerning the fMRI dataset, the available acquisitions in the database are quite limited, since resting-state protocol has been included only in the second phase of ADNI, 5 years after the beginning of the PET and the MRI data collection. Only 211 fMRI acquisitions at baseline are currently available. When available, we used repeated fMRI acquisitions from the same subjects, as we expect that more data will capture more variability and will improve the generalization of the learned models. A total of 694 fMRI scans from 211 subjects have been used in this study. However, learning on longitudinal data can yield biased and overfitted models if images from the same subject are included both in the training and the testing sets. To avoid such effects, we use a training/testing split at the subject level rather than at the acquisition level, while keeping the proportions between the clinical groups. The model training is done on all scans of all subjects included in the training phase. The model is tested on the remaining scans. The predictions are averaged for all scans of a given subject. A majority voting can be considered instead but we kept the averaging since we have a small number of scans per subject (five scans at most).

Image Acquisition and Preprocessing

The preprocessing of PET and fMRI scans was performed utilizing a combination of the Statistical Parametric Mapping (SPM12) software (www.fil.ion.ucl.ac.uk/spm/) for the classical preprocessing tasks, and the Nilearn Library (nilearn.github.io) [START_REF] Abraham | Machine learning for neuroimaging with scikit-learn[END_REF] for the temporal preprocessing and the timeserie extraction. The acquisition and the preprocessing steps are detailed in the following subsections. In addition to the usual prepro- cessing steps, the resulting images have been resampled in the same spatial referential (namely MNI152), so that images from both modalities can be mapped at the voxel level.

FDG-PET Images

FDG-PET images were acquired using a 18F-labelled fluoro-deoxyglucose (18F-FDG) radiotracer. SIEMENS, GE and PHILIPS PET scanners according to one of three standard protocols (30 -60 minute dynamic, 30 -60 minute static, 0 -60 minute dynamic). The FDG-PET images used in this study were downloaded from the ADNI database website and were already preprocessed. Each PET image is coregistered to the first frame and the sequence is averaged into one frame. Then, the averaged images are standardized to have a uniform resolution and a voxel size of 3×3×3 mm values (SUV) of PET scans from different scanners can be compared.

rs-fMRI Images

All rs-fMRI data were acquired on 3.0 Tesla PHILIPS scanners at multiple sites. These scans are about 3 mm isotropic, with TE = 30 ms and TR = 3000 ms. The rs-fMRI sequence consists of 140 volumes (timepoints). Each frame has a shape of 64 × 64 × 48 voxels. For the preprocessing of each acquisition, the first 5 frames are discarded. The remaining 135 frames of the acquisition are motion-corrected and coregistered to the corresponding T1 structural image. We use the DARTEL algorithm [START_REF] Ashburner | A fast diffeomorphic image registration algorithm[END_REF] to normalize the images; basically, a template is built at the group-level and each fMRI acquisition is non-linearly deformed to this template. The images are then spatially standardized and resampled into the MNI space, and smoothed using a Gaussian kernel of 6 mm FWHM. Timeseries are detrended for signal drift, and filtered with a 0.01 -0.1 Hz bandpass filter. In addition, some nuisance variables are removed like the global mean signal, the white matter and the cerebral spinal fluid signals.

Experiment Settings

The experiments are performed on the datasets described in 3.2. The transmodal learning is performed and compared to learning on fMRI only, either with region-to-region or region-to-voxel connectivity. Then, atlas and classifier impact on the model are assessed by comparing different selections, in order to decide which atlas and classifier should be used. Finally, we experiment the stacking approach in a multi-modal classification by including features from complementary modalities.

Our aim is to study and predict conversion of MCI subjects to AD. However, the rs-fMRI protocol has been integrated only recently in the ADNI study (from ADNI-GO phase), and only 5 MCI converters in the database come with fMRI data. We thus consider a proxy by estimating the binary classification models to discriminate AD against MCI subjects. Classification models are assessed through the accuracy defined as the proportion (in %) of the correctly classified samples from all the samples of the test set. We use a stratified leave-k-out cross-validation scheme (k is 25% of the dataset size), where training and testing splits are randomized 100 times (see Fig. 2). Model hyper-parameters (α, λ) are set by a nested cross-validation in each randomization. It relies on a 4-fold cross-validation applied on the training set (75% of the dataset) to tune the prior scaling and the regularization parameters in (4). Classifier comparison is done by comparing the mean and the standard deviation of the accuracies, as well as a two-sided Wilcoxon significance test. All experiments were implemented in Python, using the scikit-learn library [START_REF] Pedregosa | Scikit-learn: Machine learning in Python[END_REF].

Results and Discussion

We present in this section experiments to demonstrate the effectiveness of the stacking approach and the metabolic prior integration to classify AD subjects from brain network connectivity on rs-fMRI data. We compare the accuracy of the classifiers when using the stacked region-to-voxel connectivity and the PET-informed model against classical approaches like region-to-region connectivity. We also study the impact of setting some parameters of the proposed approach, such as the brain atlas or the classification model. Then, we show the extensibility of the stacking framework to integrate multi-modal and non-image features that improve the overall accuracy of AD prediction.

AD Classification by Stacking rs-fMRI Connectivity Maps

In this experiment, we show how stacking region-to-voxel connectivity maps without the PET prior predicts AD by comparing it to the classification from region-to-region connectivity matrices. For this, we compute the learning curves for the two connectivity approaches, see Fig. 3. The learning curve represents variations of the classifier accuracy when increasing the number of samples during model training.

The first information from these curves is that the region-to-voxel stacking approach significantly outperforms the classification with the connectivity matrix between regions. This can be explained by the fact that the information is restricted in the region-to-region connectivity to the selected set of regions and does not capture the functional activity in the remaining brain structures. By contrast, the region-to-voxel approach with multiple ROIs is more exhaustive as it covers the whole brain, so that the classifier takes into account potentially more discriminative information. Previous experiments revealed also that stacking connectivities from several ROIs performs better than using a single ROI [START_REF] Rahim | Integrating multimodal priors in predictive models for the functional characterization of alzheimer's disease[END_REF].

Second, we observe the benefit of including subject longitudinal acquisitions and averaging predictions of these acquisitions per subject rather than using single rs-fMRI at baseline. This improves slightly, yet consistently the accuracy, which was expected, since the training is done on three times more data.

Thirdly, we note that for all classification models considered, having more subjects in the training phase improves the classification accuracy, as all the curves display an increase with the number of samples. This suggests that using rs-fMRI as an imaging biomarker is relevant and could be even more accurate by enriching the classifier with more data. In this sense, the purpose of the integration of knowledge learned from the FDG-PET is to overcome the limitations imposed by the dataset size on the classifier accuracy. This will be discussed in section 4.3.

In summary, this experiment validates the stacking approach and the averaging of multiple datasets per subject. We study in the next section the impact of the classifiers in order to decide which model should be used for the stacking approach.

Which Classifier Should Be Used for Stacking?

We have presented in section 2.4 the stacking approach. On one hand, we propose to use a linear classifier that is well suited to the region-to-voxel connectivity, as the model coefficients provide explicitly an estimate of the discriminative patterns between the clinical groups at the voxel level. On the other hand, there is no constraint on what classifier should be used for stacking the predictions. In order to select the best pair of classifiers, we compare several linear models for the connectivity predictions combined with the following three classifiers for the stacking step: logistic regression, ridge regression and random forests. Table 3 reports the mean and the standard deviations of the AD/MCI classification accuracies with different classifiers tested. Each row is a linear model for the connectivity classification, while columns represent the stacking classifier. The main observation from the results is that all classifier combinations give almost the same mean accuracy around 77%. The choice of the linear model for region-to-voxel connectivity has no impact on accuracy. However, when stacking predictions, random forests classifier is more stable than logistic regression and ridge classifier. Standard deviations of accuracies over the runs is indeed significantly reduced. This is explained by the nature of random forests, which are an ensemble method where the prediction accuracy is stabilized by the internal averaging step; in other settings, this has also been reported to have a beneficial effect for prediction accuracy, even with no tuning [START_REF] Breiman | Random Forests[END_REF].

Region to voxel connectivity

Region to region connectivity

Functional Connectivity Based Classification with Metabolic Prior Integration

We analyze in this experiment the effect of the metabolic prior integration on the classification of AD subjects from rs-fMRI. Box plots in Fig. 4 represent the variations of the classification accuracy over 100 randomizations when using different approaches (region-to-region connectivity, region-to-voxel connectivity and the PET informed region-to-voxel connectivity) with four brain atlases. The results show that the proposed classification scheme with the metabolic prior method outperformed pure functional connectivity methods.

Accuracy improvements are about 5% without prior: 77%, with prior: 82%) and are statistically significant. This means that the discriminative patterns learned from metabolism data helped to recover a better model fron the functional connectivity data. The prior is learned from FDG-PET data from the dataset described in Table 2-b. Basically, a linear classifier was estimated over this dataset, where metabolism values on whole brain voxels are the features of the model. We used a logistic regression for the prior estimation. Other linear classifiers have been tested and we did not find major differences in term of accuracy and model coefficient distribution. Resulting classifier weights are the discriminative coefficients between the metabolic activity of AD and MCI subjects. Fig. 5-a gives a plot of these coefficients after standardization and thresholding. The main discriminative structures are the typical cerebral regions, such as the posterior cingulate cortex (PCC), the precuneus, and parts of the parietal lobe. We find in these regions a metabolism that is significantly decreased for subjects with AD, which is in agreement with the findings in many studies, such as in [START_REF] Mosconi | Multicenter standardized 18F-FDG PET diagnosis of mild cognitive impairment, Alzheimer's disease, and other dementias[END_REF]. These coefficients constitute the metabolic prior map that constrains the classification of the functional connectivity from rs-fMRI. The model accuracy in Fig. 4 (last box plot on the right) validates the statistical power of the metabolic prior. The mean accuracy (88%) is similar to state-of-the-art results on FDG-PET data from ADNI database, although we cannot compare directly scores of studies with different subsets and features.

An interesting property of the stacking model is that we can recover the discriminative spatial map between AD and MCI subjects, since we perform a voxel-level brain analysis. The learned brain spatial models consist of averaged classifier weights across leave-k-out folds. Note that the resulting map is also an average of the coefficient maps of all ROIs, weighted by the importance of the ROI from the stacking with the random forests classifier. Fig. 5-b shows the normalized and thresholded coefficient map of the regionto-voxel connectivity based model without the FDG-PET prior. Although patterns are quite noisy, these regions describe some meaningful functional structures such as the default mode network, and parts of the parietal lobe. The impact of the FDG-PET prior is shown in Fig. 5-c, where we see that the metabolic prior overcame the limitations of connectivity-based discriminant patterns. We observe in particular patterns that are smoother than with fMRI only, e.g. the clearly outstanding default mode network. This finding is in agreement with AD studies that showed functional connectivity differences observed in rs-fMRI [START_REF] Greicius | Default-mode network activity distinguishes Alzheimer's disease from healthy aging:evidence from functional MRI[END_REF], but it is hard to obtain from small rs-fMRI datasets.

Impact of the Brain Atlas on the Classification

We assess the impact of choosing a given ROIs-defining atlas on classification accuracy. For this, we compare four atlases through AD/MCI classification. These atlases have different numbers of regions and have different nature (functional, structural, histological). The first functional atlas (MSDL) has been proposed in [START_REF] Varoquaux | Multi-subject dictionary learning to segment an atlas of brain spontaneous activity[END_REF], it contains 39 ROIs learned from rs-fMRI data by a group-level dictionary-learning decomposition. The second atlas (Mayo Clinic) comprises 68 ROIs extracted from a functional dataset and proposed in [START_REF] Jones | Nonstationarity in the "resting brain's" modular architecture[END_REF]. It was constructed on 892 subjects by an independent component analysis. It yields a detailed decomposition of the default mode network, and was successfully used to characterize differences between AD and cognitively normal subjects. The third and fourth atlases are respectively structural and histological atlases. The Harvard-Oxford (96 cortical ROIs) and the Jülich (120 ROIs) atlases were obtained from FMRIB Software Library (FSL [START_REF] Smith | Advances in functional and structural mr image analysis and implementation as fsl[END_REF]) 1 The stacking + prior approach is more accurate than stacking without prior or using region-to-region connectivity. Four different atlases are compared here : i) MSDL functional atlas [START_REF] Varoquaux | Multi-subject dictionary learning to segment an atlas of brain spontaneous activity[END_REF] (39 regions). ii) Mayo Clinic functional atlas [START_REF] Jones | Nonstationarity in the "resting brain's" modular architecture[END_REF] (68 regions). iii) Harvard-Oxford anatomical atlas (96 regions). iv) Jülich Anatomical atlas (120 regions). The inter-atlas comparisons show that there are no major differences in the accuracy.

MSDL atlas Mayo Clinic atlas

The accuracy obtained with the competing atlases is shown in Fig. 4, where the results are grouped according to the classification approach. The atlas choice has no impact on the region-to-voxel connectivity without the metabolic prior, as classification accuracies are almost similar. When integrating the metabolic prior, we observe that functional atlases are more accurate than structural atlases, although the differences are not statistically significant. This means that the metabolic prior may not fit well with connectivity maps of non functional regions, and that adding non relevant regions does not improve stacking accuracy. Regarding the region-to-region connectivity, increasing the number of ROIs gives better accuracy. This effect is explained by the fact that taking more ROIs will include more brain structures that may have discriminative information. Overall, the complexity of the model used yields different bias/variance compromises that condition the choice of the atlas.

Extension to Multi-modal Stacking

Here we demonstrate how the stacking framework can be extended to multimodal data. This experiment also evaluates the relevance of rs-fMRI con- nectivity informed by metabolic prior in a multimodal classification context of AD. Many works have shown that combining complementary information about anatomical features from structural MRI, functional features from FDG-PET, and biological features from cerbrospinal fluid (CSF) leads to a more accurate prediction of AD [START_REF] Walhovd | Combining MR imaging, positron-emission tomography, and CSF biomarkers in the diagnosis and prognosis of Alzheimer disease[END_REF], [START_REF] Hinrichs | Predictive markers for AD in a multi-modality framework: An analysis of MCI progression in the ADNI population[END_REF]. In our setting, we combine PETinformed fMRI connectivities from fMRI dataset at baseline with features from anatomical MRI and CSF. These features were collected from processed and quality-checked data uploaded in ADNI database. For the CSF, three biomarkers measurements (Aβ 1-42 , t-tau, p-tau 181 ) were extracted from an analysis done at the University of Pennsylvania [START_REF] Shaw | Qualification of the analytical and clinical performance of CSF biomarker analyses in ADNI[END_REF]. Regarding the anatomical MRI, we selected sixteen volumetric features of segmented hippocampus from a processing using FreeSurfer software performed at the university of California San Francisco. Thus, each subject is represented by a feature vector composed of the three modalities. We follow the same cross-validation scheme as presented in 3.4, by using a stratified leave-25%-out randomized over 100 runs.

Fig. 6 summarizes accuracies of the AD/MCI classification on each single set separately (PET-informed connectivity, CSF biomarkers, hippocampus volumetry), then by combining modalities either by concatenating or by applying multiple kernel learning, and by stacking predictions from each modality classifier. The main message from these results is that the functional connectivity is a biomarker that brings a complementary information to reference biomarkers like CSF proteins and hippocampus volumetry, since combining these features yields better classification accuracy than using a single modality, which is in agreement with recent studies on predicting AD with rs-fMRI [START_REF] Kebets | Predicting pure amnestic mild cognitive impairment conversion to alzheimer's disease using joint modeling of imaging and clinical data[END_REF]. This suggests that fMRI could be used as a noninvasive alternative of the FDG-PET as a functional biomarker of AD. Moreover, stacking predictions of each modality is also a valid way to combine multimodal data as the accuracies (87%) are better than the concatenation (84%). Existing studies proposed to combine heterogeneous features from ADNI database for AD prediction, such as using multiple kernel learning [START_REF] Zhang | Multimodal classification of Alzheimer's disease and mild cognitive impairment[END_REF], or random forests classifier [START_REF] Gray | Random forest-based similarity measures for multi-modal classification of Alzheimer's disease[END_REF]. We cannot compare directly our accuracy scores to the ones from the papers cited above since datasets are different. Hence we applied MKL on our dataset, by using an implementation of the MKL proposed in [START_REF] Rakotomamonjy | Simplemkl[END_REF] on linear sub-kernels. We found that accuracies are in the same range as stacking (MKL accuracy: 86%, stacking accuracy: 87%). The benefit of using the stacking approach is that it enables explicit interpretation of heterogeneous modalities and can leverage variable importance computed by random forests. Indeed, each variable for the stacking represents a prediction from a distinct modality, so that the discriminative maps are recovered for example by returning to the first-level classifiers. It is also flexible to different classification schemes, since no assumptions are made on the classifier.

Conclusion

We have introduced transmodal learning in neuroimaging-based diagnosis, by enhancing classification of AD subjects from rs-fMRI with a data-driven Multimodal AD/MCI classification accuracy with the stacking framework. The modalities used are: PET-informed and stacked connectivity from fMRI, cerebrospinal fluid (CSF) biomarkers (Aβ 1-42 , t-tau, p-tau 181 ) and hippocampus volumetric features extracted from anatomical MRI. The results show that the functional information from connectivity adds accuracy to the hippocampus volumetry which is considered as a reference biomarker for AD. The stacking framework can be easily extended to a multi-modal framework. The stacking accuracies are comparable to multiple kernel learning and are better than feature concatenation. FDG-PET prior learned from a distinct and larger cohort. Specifically, we call transmodal an approach using an imaging modality to inform another imaging modality on different subjects. Rather than combining multi-modal images, transmodal imaging does not rely on having images of both modalities from the same subjects. From an application perspective it enriches an imaging modality without requiring additional measurements.

For our application, experimental results confirm that metabolic activity of brain structures measured on FDG-PET images is linked to connectivity measured with resting-state fMRI. The transmodal approach improves biomarker performance on a noninvasive modality (rs-fMRI) using an invasive but more sensitive modality (FDG-PET). Hence the metabolic prior can be used for further rs-fMRI acquisitions dataset without having recourse to FDG-PET acquisitions and to a multimodal analysis. Such an approach overcomes the limitations of fMRI (non-quantitative, low SNR), yielding accurate predictions of AD based on functional connectivity.

We find that characterizing functional networks on rs-fMRI with multiple region-to-voxel connectivity maps gives a more accurate discrimination of AD than region-to-region connectivity. We mitigate the difficult choice of seed ROI by combining maps derived from an atlas of ROIs that covers sufficiently the cortical surface. Then a stacking step combines the set of maps and improves prediction accuracy. We find that using random forests for stacking gives predictions that are not only accurate, but also stable. In addition, stacking is interpretable as it recovers discriminative maps related to the state-of-the-art on AD. Finally, stacking is interesting as it can be extended to multimodal data. Indeed, fMRI is used here as an indicator of functional activity, but anatomical imaging and non-imaging markers capture other complementary aspects of disease progression. Combining these in the stacking step improves the accuracy of the predictive model. Future work will tackle the high dimensionality of voxel-based (whole brain) connectivity. Dimensionality reduction with clustering methods might be useful to solve this problem [START_REF] Thirion | Which fMRI clustering gives good brain parcellations?[END_REF][START_REF] Michel | A supervised clustering approach for fmri-based inference of brain states[END_REF]. Also, the metabolic prior may not be suitable for some ROIs that are not related to the main resting-state networks. Regarding the dataset size, we observe that learning on multiple scans per subject improves the model. In this sense, existing datasets are a very useful resource to investigate more difficult questions such as the prognosis of conversion from MCI to AD. A truly longitudinal approach, making use of time-evolution data for each subject, would probably yield even better results, and provide new insights on the pathology progression and its impact on the resting-state networks.

Figure 3 :

 3 Figure 3: Learning curves of AD/MCI classification based on functional connectivity features. We observe that: i) The stacked regionto-voxel connectivity models are more accurate than the region-to-region connectivity models. ii) Using the subject longitudinal data in the training set gives better accuracy than using only one rs-fMRI per subject. iii) Including more samples in the training set improves the prediction accuracy in the testing set.

Figure 4 :

 4 Figure4: Impact of the transmodal connectivity on AD/MCI classification. The stacking + prior approach is more accurate than stacking without prior or using region-to-region connectivity. Four different atlases are compared here : i) MSDL functional atlas[START_REF] Varoquaux | Multi-subject dictionary learning to segment an atlas of brain spontaneous activity[END_REF] (39 regions). ii) Mayo Clinic functional atlas[START_REF] Jones | Nonstationarity in the "resting brain's" modular architecture[END_REF] (68 regions). iii) Harvard-Oxford anatomical atlas (96 regions). iv) Jülich Anatomical atlas (120 regions). The inter-atlas comparisons show that there are no major differences in the accuracy.

Figure 5 :

 5 Figure 5: Spatial maps of AD/MCI classification coefficients. The parts of the default-mode network (involved in AD characterization) are better recovered from the prior-informed stacking model than the stacking model without prior, since it has been imposed during the regularization. The final stacking map is an aggregate of atlas ROIs weighted by stacking model coefficients. The maps are normalized and thresholded.

  Figure 6:Multimodal AD/MCI classification accuracy with the stacking framework. The modalities used are: PET-informed and stacked connectivity from fMRI, cerebrospinal fluid (CSF) biomarkers (Aβ 1-42 , t-tau, p-tau 181 ) and hippocampus volumetric features extracted from anatomical MRI. The results show that the functional information from connectivity adds accuracy to the hippocampus volumetry which is considered as a reference biomarker for AD. The stacking framework can be easily extended to a multi-modal framework. The stacking accuracies are comparable to multiple kernel learning and are better than feature concatenation.

Table 1 :

 1 Notations used in the method description.

Table 2 -

 2 a and Table2-b summarize phenotype informations of the fMRI dataset and the PET dataset respectively. There are three possible values for the subject diagnosis: i) Alzheimer's Disease (AD) when the pathology has a histological confirmation by analyzing the cerebral spinal fluid collected from lumbar puncture; ii) Mild Cognitive Impairment (MCI) diagnosis gathers a spectrum from cognitive issues to prodromal stages of AD; this clinical group is generally determined through the mini-mental state examination score (MMSE) which relies on a test of functions like attention, calculation, recall, orientation, etc; iii) Cognitively Normal (CN) diagnosis is assigned if no evidence of a cognitive decline was detected during the MMSE (scores up to 30).

Table 2 :

 2 Demographic and neuropsychological characteristics. The fMRI dataset is used for the classification, and the FDG-PET dataset is used for the prior computation. The subjects from the fMRI dataset are different than the ones from the FDG-PET dataset. We have more fMRI scans than subjects since we selected longitudinal fMRI acquisitions to have a better model generalization.

		(a) -fMRI dataset
	Diagnosis N. Subj. N. scans Males	Age	MMSE
	AD	36	117	44% 72.6 ± 6.8 21.8 ± 3.0
	MCI	98	360	52% 72.2 ± 7.8 27.6 ± 2.0
	CN	77	217	40% 74.2 ± 6.6 29.0 ± 1.1
		(b) -FDG-PET dataset
	Diagnosis N. Subj. Males	Age	MMSE
	AD	273	58 % 74.9 ± 7.2 21.9 ± 3.4
	MCI	665	58 % 73.4 ± 7.5 27.4 ± 2.2
	CN	433	48 % 75.0 ± 7.1 29.1 ± 1.0

(AD: Alzheimer's Disease. MCI: Mild Cogntive Impairment. CN: Cognitively Normal. MMSE: Mini-Mental State Examination.)

  3 . FDG-PET image intensities are normalized to those of the pons so that the standard uptake Figure2: Training/testing split scheme used on rs-fMRI data. As the number of subjects is quite small, longitudinal acquisitions are included during the training phase. In order to avoid subject over-fitting, the split is done over the subjects and the prediction is obtained by averaging across acquisitions over each subject. The train/test split is iterated across 100 reshufflings.

		Training set
	Subject 1 fMRI 1 fMRI 2 fMRI 3	Subject 2 fMRI 1	...	Subject Ntrain fMRI 1 fMRI 2
		Testing set
	fMRI 1 fMRI 2 Subject Ntrain+1	...	fMRI 1 fMRI 2 fMRI 3 Subject N
	Averaged		Averaged
	predictions		predictions

Table 3 :

 3 AD prediction accuracies according to several classifiers. While the mean accuracy is almost the same, stacking the connectivity predictions with Random Forests reduces the variance.

			Stacking
		Ridge	Logistic Reg. Random Forests
	Connectivity	Ridge Logistic Reg. 76.9 ± 8.6 77.2 ± 8.9 Linear SVC 77.2 ± 8.8	77.4 ± 8.4 77.0 ± 8.6 76.9 ± 8.3	77.3 ± 4.3 76.8 ± 4.3 76.8 ± 4.4

  ing, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer's Association; Alzheimer's Drug Discovery Foundation; Araclon Biotech; Bio-Clinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer's Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.

For more details about these two atlases see fsl.fmrib.ox.ac.uk/fsl/fslwiki/ Atlases.

Acknowledgements

This work is supported by the Lidex PIM project funded by the IDEX Paris-Saclay, ANR-11-IDEX-003-02. We also acknowledge funding from the NiConnect project (ANR-11-BINF-0004 NiConnect).

Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Ag-


Sebastian Marichal
email: sebastian.marichal@upf.edu

Adrien Malaisé

Valerio Modugno

Oriane Dermy

François Charpillet

Serena Ivaldi

One-shot Evaluation of the Control Interface of a Robotic Arm by Non-Experts

Keywords: Human-robot interfaces, user evaluation, individual factors, nonexperts

In this paper we study the relation between the performance of use and user preferences for a robotic arm control interface. We are interested in the user preference of non-experts after a one-shot evaluation of the interfaces on a test task. We also probe into the possible relation between user performance and individual factors. After a focus group study, we choose to compare the robotic arm joystick and a graphical user interface. Then, we studied the user performance and subjective evaluation of the interfaces during an experiment with the robot arm Jaco and N=23 healthy adults. Our preliminary results show that the user preference for a particular interface does not seem to depend on their performance in using it: for example, many users expressed their preference for the joystick while they were better performing with the graphical interface. Contrary to our expectations, this result does not seem to relate to the user's individual factors that we evaluated, namely desire for control and negative attitude towards robots.

INTRODUCTION

In this paper, we address the question of the preference for a robotic interface by non-experts (or naive users without training in robotics), after one single evaluation of such an interface on a simple task. This refers to situations when non-experts face the decision of adopting a robot for episodic use (i.e., not a regular continuous use as workers in factories): the ease of use of an interface is crucial for the robot acceptance. We do not target users that could have or will have the time to receive a proper training on how to use a robot. While in manufacturing, robots are used by skilled workers that receive a proper training for operating the robots, this training is not likely to happen for many assistance and service scenarios: for example, inside an healthcare facility it is likely that the nurses or the patients will never receive a proper training for operating and interacting with the robots. The question arises on how to make the robot easily controllable by such users and facilitate their interaction with the robot. As the interface for controlling the robot is an essential part of the robotics system, this question impacts not only the interaction performance, but also the user acceptance and final adoption of the technology. In this study, we focus on the Kinova Jaco (see Fig. 1), a lightweight robotic arm which can be controlled with a built-in joystick. It was designed for a daily and regular use for ordinary people after some training: the joystick is easy to manipulate but it has several buttons and control modes that require practice to achieve a fluent interaction. Here, we target a different use and a one-shot evaluation: if the control interface is an obstacle to the use of the robot, the users will not likely adopt the robot even for sporadic use. Several interfaces for robot control have been investigated in HRI. For example [START_REF] Novanda | What Communication Modalities Do Users Prefer in Real Time HRI?[END_REF] investigated touch, speech and gestures for teaching a robot a nursery rhyme, finding that users do not prefer a particular modality but enjoy less touching the robot. In [START_REF] Gleeson | Tap and Push: Assessing the Value of Direct Physical Control in Human-Robot Collaborative Tasks[END_REF] the authors compared haptic interfaces with buttons, finding that users preferred buttons for simple tasks and physical command for complex tasks requiring high precision. Here, We compare the joystick with a ad-hoc graphical user interface (GUI) with buttons.

We are here interested in (i) probing the relation between individual factors and user performances for robot interfaces, and (ii) studying the relation between the performances that the user achieve with such interfaces and their preference.

Our main hypothesis is that the preference of an interface is related to the performance of using it. This premise is evident from other studies focused on interfaces evaluation. Guo & Sharlin noted that preferences for a tangible interface was related to a stronger performance in using it [START_REF] Guo | Exploring the Use of Tangible User Interfaces for Human-Robot Interaction: A Comparative Study[END_REF]. Many studies on control interfaces for robots focused on graphical user interfaces for their better acceptance by non-experts, for example [START_REF] Rouanet | A robotic game to evaluate interfaces used to show and teach visual objects to a robot in real world condition[END_REF] for teaching objects to a robot, [4] for applications in rehabilitation and medicine. In [START_REF] Chung | The Jacontrol: development of a smartphone interface for the assistive robotic manipulator[END_REF] the authors proposed an Android interface for moving the Jaco arm, but unfortunately it was not thoroughly evaluated by final users.

Our second hypothesis is that individual factors, such as traits and attitudes, may influence the user performances with the robot interfaces. There is indeed prior evidence that some personality traits have significant effects on the perceived ease of use of new technologies, such as smartphones [START_REF] Ozbek | The impact of personality on Technology acceptance: a study on smart phone users[END_REF]. There is also evidence that personality traits and attitudes have some influence in HRI in the context of social robotics [START_REF] Ivaldi | Towards engagement models that consider individual factors in HRI: on the relation of extroversion and negative attitude towards robots to gaze and speech during a human-robot assembly task[END_REF]. It seems therefore rational to explore the relation between individual factors and the user perception and performance in controlling a robot. Two attitudes seems particularly relevant for our study: the Negative Attitude towards robots (NARS) [START_REF] Nomura | Experimental investigation into influence of negative attitudes toward robots on human-robot interaction[END_REF], which captures the anxiety of an imagined interaction with a robot, and the Desire For Control (DFC) [START_REF] Burger | The desirability of Control[END_REF], which captures the attitude to be in control or control situations. The first could influence for example the time spent on using the robot, while the second could influence the preference for an interface that provides a stronger sensation of controlling the robot.

Our study was split in two phases. In the first, we carried out a focus group study to identify the main concerns of people interacting with a robotic arm, the key elements underlying their imagined interaction and the imagined interfaces to control the robot movement. This set enabled us to formulate the first hypothesis and choose a graphical user interface (GUI) as an intuitive interface alternative to the Kinova joystick. The second phase concerned the experiments with the Jaco robot and the two interfaces. We first performed a pilot study with University students to test the experimental setup and gain preliminary insights for the later final experiments with ordinary adults. The analysis of the pilot study and the outcome of the focus group enabled us to refine the evaluation questionnaires to be used for the final experiments and formulate new hypothesis.

We studied the user performance and subjective evaluation of the interfaces during an experiment with the robot arm Jaco and N=23 healthy adults. We provide quantitative evidence of the different performances obtained by nonexperts, using both interfaces for the first time to realize some tasks. We also report on the user feedback in using the two interfaces, which provides us useful information to inform future interface designers.

Our preliminary results show that the user preference for a particular interface does not seem to depend on their performance in using it: for example, many users expressed their preference for the joystick whereas they were better performing with the graphical interface. Also, contrary to our expectations, this result does not seem to relate to the user's aforementioned individual factors.

Research Hypothesis -Given the previous results in the literature, we expect that the GUI will be easier to use than the joystick, for non-trained users. The GUI has the advantage to not require too much training, and it provides some graphical shortcuts to the main robot configurations. To provide a quantitative measure of the ease of use, we use the duration of execution of tasks performed with an interface, and the number of errors done while using it. We formulate the hypothesis as: H1) The time necessary to complete the tasks with the GUI is shorter than with the joystick. H2) The number of precision errors with the GUI is lower than with the joystick. H3) The number of mapping errors with the GUI is lower than with the joystick. We also hypothesize that the user personality, attitudes and their prior experience with related technologies may influence the user acceptance of the proposed technologies and the performance in using it. The desire for control could play a crucial role in the preference for the joystick to the GUI, as the users could have the impression to be more in control of the robot while moving it. The negative attitude towards robots could influence the user perception of the interaction and the perceived ease of use. We formulate therefore the following hypothesis: H4) Participants with high score of DFC will prefer the joystick to the GUI. H5) Participants with a high negative attitude towards robots score will make more errors and have a lower perceived ease of use and user satisfaction.

METHODS

Participants

The participants were all French, healthy adults that volunteered to take part in the study. The focus group study was carried out with 6 adults (age: 39.16 ± 15.71, 3 males, 3 females) without or with little robotics experience (1 participant). The pilot study was carried out with 7 University students in cognitive sciences (age: 23.14 ± 1.46, 2 males, 5 females). The final experiments with the robot were carried out with 23 adults (age: 35.13 ± 11.98, 12 males, 11 females) without robotics experience.

Experimental setup

The experiments were carried out at the LARSEN laboratory of INRIA (Nancy, France). The experimental setup was organized as shown in Fig. 1. A desk with a laptop was placed in front of the Kinova Jaco arm, fixed on a table. The arm was positioned in such a way to be able to perform some manipulations on the ADL setup (Fig. 2C), made of two boxes: one with a door handle, one with three buttons and a drawer containing a small object. A video camera, placed behind the participants, was used to record the experiments. Two interfaces (see Fig. 2A-B) for controlling the robot were used: the native joystick by Kinova and our own ad-hoc graphical user interface (GUI). The joystick can move the hand in the Cartesian space (position and rotation), open and close the fingers. Two buttons are used to select whether to move the hand position (mode 1), its orientation (mode 2) or the fingers (mode 3). The GUI was developed with Qt and is open-source4 . Both interfaces use the same Kinova API for robot control and inverse kinematics solving.

Questionnaires

To probe into the influence of individual factors, we asked the participants to the robot experiment to fill out some questionnaires before the experiments: the Negative Attitude Towards Robots Scale (NARS) [START_REF] Nomura | Experimental investigation into influence of negative attitudes toward robots on human-robot interaction[END_REF] and the Desire For Control scale (DFC) [START_REF] Burger | The desirability of Control[END_REF]. Our French adaptation was used [START_REF] Ivaldi | Towards engagement models that consider individual factors in HRI: on the relation of extroversion and negative attitude towards robots to gaze and speech during a human-robot assembly task[END_REF]. The participants also filled two post-experimental questionnaires consisting of questions/affirmations adapted from usability and technology acceptance models to a robotic context as it was done in previous works [START_REF] Heerink | Measuring the influence of social abilities on acceptance of an interface robot and a screen agent by elderly users[END_REF][START_REF] Benmessaoud | Facilitators and Barriers to Adopting Robotic-Assisted Surgery: Contextualizing the Unified Theory of Acceptance and Use of Technology[END_REF]. The post-block questionnaire, at the end of each experimental condition (block when one interface is used), was based on the USE questionnaire [START_REF] Lund | Measuring Usability with the USE Questionnaire[END_REF] (typical questions were "How good will you rate the movement you achieved in the 'open the drawer' task?"). The post-experimental questionnaire consisted of a set of affirmations to be rated on a 7-points Likert scale, targeting constructs typical of the UTAUT [START_REF] Venkatesh | User Acceptance of Information Technology: Toward a Unified View[END_REF] and TAM 3 models [START_REF] Venkatesh | Technology Acceptance Model 3 and a Research Agenda on Interventions[END_REF] (typical questions were "Controlling the robot with the GUI is easy").

Experimental protocol

The study consist of a focus group and two robot experiments: a pilot study with University students, then experiments with ordinary adults. All the data were recorded in anonymous form through a random numerical id attributed to each participant. All participants were equally informed by the experimenter about the purpose of the study and their rights, according to the ethics guidelines of our institute. An informed consent form was signed by each participant. The protocol received the positive approbation of the local Ethics Committee.

Focus group study -We asked a group of 6 adults without or with little experience in robotics to imagine how they would interact with the robot and control it to do some tasks. The group gathered in a closed room around a table. One moderator led the group, while two recorders took notes and annotated sentences and body language. The session lasted about 2 hours and was recorded for analysis purposes. The experimenter asked to the group six warm-up questions, such as "Tell us about your overall experience with robots ", "In which situation(s) do you imagine that a robotic arm such as the Kinova would be useful? ". In a work in pairs, participants had to present their ideas about interfaces for controlling a robot arm.

Pilot study with the robot -We carried out a pilot study with the Jaco robot and 7 University students. Each participant had to perform the 4 tasks (see Figure 2C) with the robot, using the joystick and the GUI. The order of the interfaces was randomized across the participants. After the experiment with the robot, we asked the participant to express their preference for one of the two interfaces and provide their feedback and personal evaluations.

Experimental study with the robot -The experiments with the Jaco robot were carried out with 23 adults without expertise in robotics. Each participant filled in the questionnaires NARS and DFC one week before the experiment. The day of the experiment, the participant was welcomed to the laboratory room by the experimenter and seated on a table with a laptop (see Fig. 1) in front of the robot. There were two blocks corresponding to the two experimental conditions: one with the joystick and one with the GUI. In each block, the participant had to perform the 4 tasks with the robot (see Fig. 2C). The order of use of the interface was randomized and balanced across the participants. To ensure that all the participants received an equal set of instructions, we provided them with the same instructions, either in paper format and in video format (tutorial). The participant started by reading some paper instructions explaining the 4 tasks to be performed with the robot. After reading the instructions, they had to rate some statements on a 7-items Likert scale, such as "The required tasks are difficult" and "The instructions were difficult to read". We also added two trick questions to check if they were attentive and had carefully read the instructions. Before each block, the participant watched a 2/3 minutes video tutorial explaining how to use each interface, then he/she could familiarize and try it for about 1 minute. We instructed the participants to follow a think-aloud protocol. When the participant was ready to start, he/she began performing the 4 tasks in sequence. Two experimenters monitored and annotated the experiment. After completion, the participant filled in a questionnaire evaluating the ease of use of the interface. The sequence tutorial-test-tasks-evaluation was repeated for the second interface. After the experiment with the robot, the participant filled in the post-experimental evaluation questionnaire, then answered to some semi-directed questions during an interview with the two experimenters.

Measures and data analysis

During the focus group, two recorders annotated the discussion. Video recordings were used to complete the annotation offline. In the pilot study, we measured the duration of each task and the user preference for each interface. In the robot experiments, we employed both objective and subjective measures. Two experimenters annotated: the duration of each task ; the numbers of precision errors, represented by the number of times the robot hit the ADL board; the number of mapping errors, represented by the number of times the robot was moved in the opposite direction with respect to the desired (we could identify this by the explicit verbalization of the participant, or by two consecutive movements in opposite directions where the first was clearly in the wrong direction with respect to the goal of the movement). The questionnaires' score for NARS and DFC were computed according to the authors' recommendations. The subjective measures retrieved from the post-experimental questionnaires are the perceived ease of use (PEOU, typical question: "Controlling the robot with the GUI is easy")), the user satisfaction (US, "How good will you rate the movement you achieved in the 'open the drawer' task?") and the facilitating condition (FC, "The time to test the Joystick before the experiment was enough") related to each interface, computed by the sum of the score of the questionnaire items for each construct. The expertise in using joysticks was a self-reported score on a 10-item scale.

Unless otherwise stated, we computed median and standard deviation of all the measured variables; we used Spearman's correlation and verified the statistical significance of the different conditions with a Wilcoxon signed ranked test with continuity correction in R.

RESULTS

Focus group -The focus group participants did not have a particular affinity with robotics, and were generally worried about the possibility of robots replacing humans. When asked about the possible use for the Jaco arm, they indicated grabbing objects on very high shelves, assisting people with impairments or arm troubles, doing manual tasks like laundry, ironing and painting walls. Almost all the participants agreed that the robot should not be completely autonomous: they need to be in control of the situation when the robot is acting. They said that they should "teach the robot to do the things the way we want" and "be able to stop the robot anytime". When we asked how to control the robot, the participants mostly indicated panels with buttons (3/6). In particular, one participant explained that there should be a button for each possible robot gesture.

Pilot study -The only significant difference in terms of task duration with the two interfaces is on the second task (opening the drawer, V=0 p=0.0156<0.05). We did not find any significant correlation between the task duration and the participants' self-report expertise with joysticks.

Concerning the joystick, the negative points were: the difficulty in controlling the hand orientation and the way to change the modes with the buttons. Positive points were that it was more intuitive to move in the x-y-z space, especially for the students used to play video-games, and that it felt like an "extension of their arm". Concerning the GUI, the negative point was that it required to switch continuously the attention from the laptop to the robot. The positive points were its clearer design that made the actions explicit and the ease of use when choosing pre-determined orientations of the hand for manipulation.

We asked the 7 participants to choose the interface that was easier to use and more intuitive for them: 2 preferred the joystick and 5 the GUI ("it can be mastered, one makes more errors with the joystick ").

Robot experiments -After reading the instructions, the participants evaluated the tasks to be not difficult (on a 7-item Likert scale, median=2, stdev=1.67) and the instructions easy to read (median=1, stdev=2.03). We found a significant difference in the overall duration of the tasks (V=25 p=0.0006<0.001) for the two conditions, in particular for Task 2 (opening the drawer, V=10 p=0.0002<0.001) and Task 3 (grabbing the object, V=28.5 p=0.0009<0.001), a fair difference for Task 4 (opening the door, V=51.5 p=0.0089<0.01). We also compared the dura-tion of the tasks executed with each interface when the latter is first or second in order of execution: we did not find difference in the execution for the GUI (Mann-Whitney, W=82 p=0.347 (N.S.)), whereas there is a weak evidence for a difference in the execution time of the joystick if it is used as first or second (Mann-Whitney, W=27.5 p=0.0193<0.05). In terms of use of the interface, there is a marginal difference in terms of precision (V=53 p=0.0531 (N.S.)), while there is a strong difference in terms of mapping errors (V=0 p=2.85e-05<0.001) -the median number of mapping errors with the joystick is also quite elevated [START_REF] Lee | The Impact of User Control Design Types on People's Perception of a Robot[END_REF]. Regarding the subjective measures retrieved by the questions, we found a significant difference in the ratings in terms of ease of use (V=251.5 p=5.23e-05<0.001), satisfaction (V=239 p=0.0022<0.005) and facilitating conditions (V=159 p=0.0013<0.005): the GUI has higher ratings than the joystick on all the three items. We did not find a significant correlation between the users' performance and their prior expertise in using joysticks nor between the user performance and their NARS.

Among the 23 participants, 11 expressed preference for the joystick and 12 for the GUI. However, in terms of usability, the joystick was favored by 6 participants, while the GUI by 16 (one participant said they were equal). We tested if the interface preference was related to the DFC score of the participants but we did not find any significant difference (Mann-Whitney, W=48, p=0.279 (N.S.)).

We asked the participants to provide their feedback in the post-experimental interview. Many participants highlighted that the joystick made them feel more "in control" when moving in the main Cartesian directions (x,y,z -the first mode of the joystick) and that they could achieve more precise movements with it. Almost all the participants reported that switching the mode with the joystick was very difficult. However, some thought that they could become good users with a dedicated training. One participant, for example, said "my son is very good with the video-games pad, he will learn in 10 minutes; for me, I will need some hours". Many participants appreciated the GUI because of the intuitive buttons where each command/action was explicit.

DISCUSSION

In this study we focused on non-expert users controlling a robot for their first time: if the robot-user does not have a proper training, or if he is using the robot only once in a while, which interface could be easier to use and facilitating the robot adoption? From the focus group study, we learned that people imagine to interact with the robot in a structured way (e.g., buttons) that allows them to be in control of the robot decisions (e.g., when to start, when to stop).

To make the robot controllable by non-experts, our conclusion is that we need a very reliable control interface that they can understand and use easily/intuitively, that is robust and that gives them the impression to be in control. From the participants suggestions, a panel with buttons seems appropriate as a control interface: it gives the user the impression that the robot can act upon their orders. For the purpose of this study, we decided that the most appropriate control interface to test against the joystick of the Jaco arm was a GUI with buttons.

Is a GUI really better than a joystick? -From the pilot study with students, we could not strongly conclude that the GUI brings notable improvements over the joystick. In the experiments with ordinary adults, the GUI is better than the joystick in terms of objective performance measures and subjective user evaluation. We found significant difference in the duration of tasks and mapping errors, but not in the precision errors: therefore we accept H1 and H3 but reject H2. Almost all participants found the GUI easier to use, more understandable and straightforward. Many participants appreciated moving the robot with the joystick as they felt it an "extension of their hand". Interestingly, while most participants appreciated the pre-programmed orientations/configurations of the hand, that were quite difficult to obtain with the joystick, some participants reported them as a constraint that was limiting their freedom to choose different orientations of the hand to realize the tasks. These participants suggested that the two interfaces should be combined to give the user more freedom. It is however important to notice that the GUI performs better than the joystick in our particular experimental conditions, where the participants have a very limited training for using the interfaces (a video tutorial and 1 minute to familiarize with the interface and try it). The results could be very different in a case where the participant uses the robot on a regular basis or receives a proper training. We will address this case in future experiments.

Do individual factors play a role in the user performance with an interface? -Our preliminary results show that the user preference for a particular interface does not seem to relate to their performance in using it: for example, many users expressed their preference for the joystick whereas they were better with the GUI. Contrary to our expectations, this result does not seem to relate to the user's individual factors, as we did not find a strong evidence to support our hypothesis. We did not find significant correlations between the user preferences or performances with both NARS and DFC. We therefore reject H4 and H5. Nevertheless, in the post-experimental interviews many participants reported to feel more comfortable with the joystick despite being better with the GUI: this may seem counter-intuitive, but in fact suggests that there may be other individual criteria that drive their choice.

CONCLUSIONS

Two main questions emerge for future work: Which are the key factors that determine user preference for a robot control interface and if the preference and performance in using an interface would change in a long term scenario (i.e., a scenario where users receive a training for operating the robot with the interface and use such an interface more frequently or on a daily basis). We plan more experiments to investigate more thoroughly all these questions.

Fig. 1 .

 1 Fig. 1. The experimental setup with the Kinova Jaco arm. The participant moves the arm using (A) the joystick and (B) the graphical interface on the laptop.

Fig. 2 .

 2 Fig. 2. The two interfaces used for the evaluation: (A) the Kinova joystick and (B) our ad-hoc graphical interface on the laptop. (C) The Activities of Daily Living setup: click the three buttons (task 1 ), open a drawer (task 2 ), take an object inside the drawer (task 3 ), open the door (task 4 ).

https://github.com/serena-ivaldi/kinova-modules