A Sebastian Schroeder

Sebastian

Nikolas ; Hesse

; Weinberger

; Tacke

; Gerstl

; Hilgendorff

; Heinen

; Arens

; Dijkstra

Pujades Rocamora

; Black

; Bodensteiner

; Hadders-Algra

General Movement Assessment from videos of computed 3D infant body models is equally effective compared to conventional RGB Video rating

Keywords: General Movement Assessment (GMA), high risk infants, early diagnosis, fidgety, automated motion analysis, cerebral palsy, Kinect

Background: General Movement Assessment (GMA) is a powerful tool to predict Cerebral Palsy (CP). Yet, GMA requires substantial training challenging its broad implementation in clinical routine. This inspired a world-wide quest for automated GMA.

Aim:

To test whether a low-cost, marker-less system for three-dimensional motion capture from RGB depth sequences using a whole body infant model may serve as the basis for automated GMA.

Study design:

Clinical case study at an academic neurodevelopmental outpatient clinic.

Subjects: Twenty-nine high risk infants were assessed at their clinical follow-up at 2-4 month corrected age (CA). Their neurodevelopmental outcome was assessed regularly up to 12-31 months CA.

Outcome measures: GMA according to Hadders-Algra by a masked GMA-expert of conventional and computed 3D body model ("SMIL motion") videos of the same GMs.

Agreement between both GMAs was tested using dichotomous and graded scaling with Kappa and intraclass correlations, respectively. Sensitivity and specificity to predict CP at ≥12 months CA were assessed.

Results: Agreement of the two GMA ratings was moderate-good for ICC=0.874 [95%CI 0.730;0.941]) and substantial-good for fidgety movements (FMs; Kappa=0.78, ICC=0.926 [95%CI 0.843;0.965]). Five children were diagnosed with CP (four bilateral, one unilateral CP). The GMs of the child with unilateral CP were twice rated as mildly abnormal with FMs. GM-complexity and somewhat less FMs, of both conventional and SMIL motion videos predicted bilateral CP comparably to published literature.

Conclusions:

Our computed infant 3D full body model is an attractive starting point for automated GMA in infants at risk of CP.

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Cerebral palsy (CP) describes the most common physical disability in children. It occurs in approximately 2/1.000 live births [START_REF] Himmelmann | The panorama of cerebral palsy in Sweden part XII shows that patterns changed in the birth years 2007-2010[END_REF]. The average age at which the diagnosis is made and communicated with the families has been shown to be approximately 11 months. For less severely affected children diagnosis may even be made as late as 24 months of age [START_REF] Granild-Jensen | Predictors for early diagnosis of cerebral palsy from national registry data[END_REF]. Yet, early and accurate detection of infants at very high risk of CP is a prerequisite for timely initiation of early intervention, i.e. intervention starting before the age of 6 months [START_REF] Novak | Early, Accurate Diagnosis and Early Intervention in Cerebral Palsy: Advances in Diagnosis and Treatment[END_REF]. Early intervention programs use caregiver coaching and address the infant's sensorimotor development, attention, self-regulation, early communication skills and parental mental wellbeing [START_REF] Ziegler | Coaching in early physical therapy intervention: the COPCA program as an example of translation of theory into practice[END_REF][START_REF] Hutchon | Early intervention programmes for infants at high risk of atypical neurodevelopmental outcome[END_REF].

To detect very high risk of CP, it is recommended to use a combination of standardized tools in conjunction with clinical history. Before 5 months corrected age (CA), the most predictive tools to detect risk of CP are term-age cerebral magnetic resonance imaging (MRI) and the video-based Qualitative Assessment of General Movements (GMA) between 2 and 5 months CA [START_REF] Bosanquet | A systematic review of tests to predict cerebral palsy in young children[END_REF][START_REF] Hadders-Algra | Early human brain development: Starring the subplate[END_REF][START_REF] Kwong | Predictive validity of spontaneous early infant movement for later cerebral palsy: a systematic review[END_REF]). Term-age MRI may, however, not serve as a screening tool in clinical practice, leaving GMA as the most predictive tool in everyday practice.

General movements (GMs) are spontaneous movements, which involve all body parts [START_REF] Prechtl | Qualitative changes of spontaneous movements in fetus and preterm infant are a marker of neurological dysfunction[END_REF][START_REF] Hadders-Algra | Quality of general movements and the development of minor neurological dysfunction at toddler and school age[END_REF].

They emerge during early fetal life and disappear when goal-directed motor behaviour emerges around 4-5 months CA. The form of typical GMs changes as a result of developmental transformations of the nervous system. In the last phase, at 2 to 5 months CA, GMs have a 'fidgety' character. Fidgety GMs (FMs) occur irregularly all over the body and consist of a continuous stream of tiny elegant movements. During each phase, typical GMs are primarily characterized by complexity and variation (in short: complexity) [START_REF] Hadders-Algra | Quality of general movements and the development of minor neurological dysfunction at toddler and school age[END_REF]. GMs are considered as abnormal when their complexity is reduced. [START_REF] Prechtl | Qualitative changes of spontaneous movements in fetus and preterm infant are a marker of neurological dysfunction[END_REF][START_REF] Hadders-Algra | Quality of general movements and the development of minor neurological dysfunction at toddler and school age[END_REF]. At fidgety agethe age at which GMA best predicts developmental outcome [START_REF] Bosanquet | A systematic review of tests to predict cerebral palsy in young children[END_REF][START_REF] Hadders-Algra | Neural substrate and clinical significance of general movements: an update[END_REF] atypical GMs are also characterized by a reduction or disappearance of the age-specific FMs. Therefore Hadders-Algra suggested that GMA at 2-5 months CA (fidgety age) is performed with a two-step procedure: (1) grading movement complexity and (2) assessing FMs [START_REF] Hadders-Algra | Neural substrate and clinical significance of general movements: an update[END_REF][START_REF] Hamer | Assessment of specific characteristics of abnormal general movements: does it enhance the prediction of cerebral palsy?[END_REF].

Unfortunately, GMA requires a high level of expertise in the raters, which impedes the application in broad clinical practice [START_REF] Adde | General movement assessment: predicting cerebral palsy in clinical practise[END_REF][START_REF] Ricci | Feasibility of Using the General Movements Assessment of Infants in the United States[END_REF][START_REF] Brown | Formal training in general movement assessment is required to effectively evaluate infants with perinatal asphyxia in outpatient settings[END_REF][START_REF] Bernhardt | Inter-and intra-observer agreement of Prechtl's method on the qualitative assessment of general movements in preterm, term and young infants[END_REF][START_REF] Datta | Fidgety movements in infants born very preterm: predictive value for cerebral palsy in a clinical multicentre setting[END_REF]. Therefore, it is desirable to develop an easy to use, automated screening tool that can be applied in general paediatric practice as an alternative for the expert dependent video ratings of GMs. To be generally applicable, this diagnostic tool should be sensitive and specific and should use low-cost, commercially available equipment that does not influence the infants' motions.

A primary prerequisite for automatic analysis of motor development is to capture infant movements with high precision. Motions are represented as continuous time-series of the kinematics of limb and trunk movements, e.g., 2-dimensional (2D) or 3-dimensional (3D) body joint positions or joint angles. In the subsequent analysis stage, motion features have to be extracted from the captured movements to serve as input to machine learning classifiers, which can be used to identify infants with abnormal motor activity or to predict the risk of CP.

The first computerized approach towards automated detection of CP used a marker-based Vicon system to track movements in 3D [START_REF] Meinecke | Movement analysis in the early detection of newborns at risk for developing spasticity due to infantile cerebral palsy[END_REF]. Relevant characteristics of the captured movements were encoded in motion features based on limb velocities and accelerations. A machine learning classifier was trained to predict the CP risk based on these features [START_REF] Meinecke | Movement analysis in the early detection of newborns at risk for developing spasticity due to infantile cerebral palsy[END_REF]. Up to date, a wide variety of sensors with different properties have been used for capturing infant movements, e.g., body-worn miniaturized movement sensors [START_REF] Heinze | Movement analysis by accelerometry of newborns and infants for the early detection of movement disorders due to infantile cerebral palsy[END_REF][START_REF] Gima | Evaluation of Fidgety Movements of Infants Based on Gestalt Perception Reflects Differences in Limb Movement Trajectory Curvature[END_REF] or magnetic tracking systems [START_REF] Philippi | Computer-based analysis of general movements reveals stereotypies predicting cerebral palsy[END_REF]. Video-based motion tracking has the advantage that it does not require the attachment of sensors [START_REF] Rahmati | Frequency-based features for early cerebral palsy prediction[END_REF][START_REF] Stahl | An optical flow-based method to predict infantile cerebral palsy[END_REF].

Marschik et al. [START_REF] Marschik | A Novel Way to Measure and Predict Development: A Heuristic Approach to Facilitate the Early Detection of Neurodevelopmental Disorders[END_REF] developed the 'iDN Fingerprint Model' that combines multiple biological signals of the young infant, including a 3D video of GMs with a Red-Green-Blue Depth (RGB-D) camera. The Italian group of Orlandi and colleagues proposed a system based on the combination of audio and video data, i.e., the infant's spontaneous vocalizations and its GMs [START_REF] Orlandi | AVIM-A contactless system for infant data acquisition and analysis: Software architecture and first results[END_REF]. However, currently both systems' psychometric properties have not been reported.

Purely computer-based 2D video analysis for the assessment of GMs was first published by Adde and colleagues [START_REF] Adde | Using computer-based video analysis in the study of fidgety movements[END_REF]. They developed a General Movement Toolbox (GMT), which uses the displacement of pixels from one video frame to the next for quantitative analyses of GMs occurring at 2-5 months CA, i.e., the fidgety GMs. Visual representations from GMT resulted in recognisable patterns of the fidgety movements. They reported that the application of the automatic movement classification in their mixed study group of 82 high and low risk infants born at term or preterm would reduce the need for further referral by 70%. Their study showed that video recordings can be used for qualitative and quantitative analyses of GMs [START_REF] Adde | Using computer-based video analysis in the study of fidgety movements[END_REF]. More recently, the same group suggested on the basis of a group of 150 high risk infants that the analysis could be used to reduce the rate of infants needing intensive developmental monitoring [START_REF] Stoen | Computer-based video analysis identifies infants with absence of fidgety movements[END_REF]. However, two things should be noted. First, the system misclassified some children, e.g., missing those who needed monitoring (10%) or referring those who did not require intensive follow-up (20%). Second, the authors had to exclude infants with a specific type of moderately abnormal GMs (abnormal FMs) from the analysis as the algorithm could not deal with these movements.

More recently, two other groups reported on computer-based analysis of 2D GM-videos. The group of Orlandi used an approach based on a skin model for segmentation and large displacement optical flow (LDOF) [START_REF] Orlandi | Detection of Atypical and Typical Infant Movements using Computer-based Video Analysis[END_REF]. The kinematic features extracted from the model were used to classify fidgety GMs as typical or atypical. Their retrospective study based on 127 videos of preterm infants showed a specificity of 99%, but a sensitivity of only 44% in predicting CP [START_REF] Orlandi | Detection of Atypical and Typical Infant Movements using Computer-based Video Analysis[END_REF]. The group of Marchi and colleagues reported an approach based on automated pose estimation to capture key aspects of GMs [START_REF] Marchi | Automated pose estimation captures key aspects of General Movements at eight to 17 weeks from conventional videos[END_REF]. They used OpenPose -an open source computer vision software -developed by Cao et al. [START_REF] Cao | Realtime multi-person 2D pose estimation using part affnity fields[END_REF] to analyse the 2D videos of 21 preselected infants at fidgety GM-age. They considered their approach as feasible for automatizing infant motion analysis. However, they indicated that pose tracking errors occurred in 14 out of 21 recorded videos, which hindered automatized analysis. They concluded, that their approach would improve significantly by the use of three dimensional (3D) instead of 2D camera technology [START_REF] Marchi | Automated pose estimation captures key aspects of General Movements at eight to 17 weeks from conventional videos[END_REF].

To summarize, up to now no results are available on the application of 3D systems for automated assessment of movements in infancy that do not use sensors/markers that have to be attached to the infants' limbs, which may affect their behaviour.

Since 2015, we have developed a motion tracking system that estimates the full body pose of infants in 3D [START_REF] Hesse | Estimating Body Pose of Infants in Depth Images using Random Ferns[END_REF][START_REF] Hesse | Markerless motion analysis for early detection of infantile movement disorders. EMBEC & NBC[END_REF][START_REF] Hesse | Body Pose Estimation in Depth Images for Infant Motion Analysis[END_REF][START_REF] Hesse | Learning and Tracking the 3D Body Shape of Freely Moving Infants from RGB-D sequences[END_REF] (Kinematic Motion Analysis Tool; KineMAT). The system is based on a commercially available, low-cost RGB-depth sensor (Kinect 1.0, Microsoft, USA) and can be applied without attaching markers to the infant's body (see Figure 1) [START_REF] Hesse | Estimating Body Pose of Infants in Depth Images using Random Ferns[END_REF][START_REF] Hesse | Markerless motion analysis for early detection of infantile movement disorders. EMBEC & NBC[END_REF][START_REF] Hesse | Body Pose Estimation in Depth Images for Infant Motion Analysis[END_REF]. Recently, we developed the Skinned Multi-Infant Linear (SMIL) body model [START_REF] Hesse | Learning and Tracking the 3D Body Shape of Freely Moving Infants from RGB-D sequences[END_REF], which is based on the adult body model SMPL (Skinned Multi-Person Linear Model) [START_REF] Loper | Smpl: A skinned multi-person linear model[END_REF]. The pose and shape of this virtual body can be adjusted with the model parameters. This allows us to use SMIL for capturing the 3D shape and 3D pose of infants from RGB-D sequences. We create a video of the resulting SMIL body reproducing the movements of the recorded infant, which we denote "SMIL motion video". Figure 2 illustrates the resemblance of the RGB and SMIL motion videos.

The aim of this study was to evaluate how reliably our developed 3D infant body model (SMIL motion video) represents true infant spontaneous movements to a human observer. To this end, we assessed the agreement on the classifications of a human expert of GM-quality of two videos of the same GMs of 29 consecutively recruited high risk infants at fidgety age. The data of both videos originated from the same camera recording. One video consisted of the conventional GM-video used in clinical practice, the other of the SMIL motion video (Fig. 1 and Fig. 2). Both videos were assessed according to Hadders-Algra, i.e., assessing GM-complexity (in the present study with three degrees of fine-grading) and assessing FMs with two degrees of grading [START_REF] Hamer | Assessment of specific characteristics of abnormal general movements: does it enhance the prediction of cerebral palsy?[END_REF][START_REF] Hadders-Algra | Evaluation of motor function in young infants by means of the assessment of general movements: a review[END_REF][START_REF] Groen | General movements in early infancy predict neuromotor development at 9 to 12 years of age[END_REF].

Material and Methods

Participants

Participants were high risk infants who received standardized follow up at the Centre of Developmental Care of the integrated Social Paediatric Centre at the University Hospital in Munich, Germany. High risk infants were infants meeting at least one of the following criteria: born ≤ 32 weeks of gestation (GA), birth weight below 1500 grams, or other perinatal complications such as term asphyxia or early onset meningitis. Exclusion criteria were additional, potentially confounding diagnoses (e.g. genetic disorders, brachial plexus palsy, neuromuscular disorders). All infants meeting the inclusion criteria were examined by the first author between November 2016 and October 2017. The video recording was performed prior to the general developmental neurological examination. The infants' prenatal, perinatal and neonatal history was documented on standardized charts. After fidgety GM-age, the infants generally were assessed at a three to six months interval depending on the clinical need. Each follow-up visit consisted of a detailed neuropaediatric clinical examination. In children ≥12 months CA the diagnosis of CP was based on clinical findings in line with international recommendations as published by Boychuk et al. [START_REF] Boychuck | International expert recommendations of clinical features to prompt referral for diagnostic assessment of cerebral palsy[END_REF]. At 24 months CA, children were invited to participate in a structured neurodevelopmental assessment using the Bayley Scales of

Methods

The infant lay in supine position and an RGB-D camera (Microsoft Kinect V1, USA) was positioned directly above the infant at a distance of 100 cm. This camera allows simultaneous recordings of regular RGB videos (typically used for clinical GMA ratings) and depth videos, which provide the 3D distance to the camera for each pixel of the image. The infant's GMs were recorded for 3 minutes in a peaceful, quiet environment during active wakefulness.

To capture the infant's motion, we use SMIL (Skinned Multi-Infant Linear body model; see introduction for additional details on the development of the model [START_REF] Hesse | Learning and Tracking the 3D Body Shape of Freely Moving Infants from RGB-D sequences[END_REF]), which models the 3D body surface together with an underlying skeleton. The pose and shape of this virtual body can be adjusted with the model parameters. This allows the "registration" of the model to RGB-D data. Registration denotes the process of (automatically) adjusting model parameters so that the model best describes the input data, i.e. the model takes the same shape and pose as the recorded infant. We create a video of the SMIL registration results, i.e., the SMIL body reproducing the recorded infant movements, which we denote as "SMIL motion video" (see Fig. 1 and Fig. 2). SMIL allows access to 3D positions of all points of the body and the skeleton, as well as body joint angles in all degrees of freedom across time. The SMIL Model achieves a metric accuracy, i.e., the average distance of RGB-D scan points to the model surface, of 2.51 mm (SD 0.21 mm) and represents the original (correct) infant body pose in 98.8% of the time [START_REF] Hesse | Learning and Tracking the 3D Body Shape of Freely Moving Infants from RGB-D sequences[END_REF]. The model is adapted to the specifics of infantile body proportions. Our registration method can handle fast movements and self-occlusions, which are a common problem when capturing motions of freely moving humans with only one camera. Further details on the registration of the SMIL Model to RGB-D sequences can be found in previous publication [START_REF] Hesse | Learning and Tracking the 3D Body Shape of Freely Moving Infants from RGB-D sequences[END_REF].

The conventional video and the SMIL motion video of each infant were used for blinded GMA rating according to Hadders-Algra. For GMA at fidgety age this means a two-step procedure.

First, the degree of GM-complexity is assessedthis is the parameter that can be assessed at any GM-age [START_REF] Hadders-Algra | Quality of general movements and the development of minor neurological dysfunction at toddler and school age[END_REF]. It supplies information on the integrity of the subcortical-cortical networks [START_REF] Hadders-Algra | Neural substrate and clinical significance of general movements: an update[END_REF]. We graded GM-complexity in three ways: (a) by using a classification into four categories: normal-optimal movements (NO; abundant complexity and variation), normal-suboptimal movements (NS; sufficient complexity and variation), mildly abnormal movements (MA; insufficient variation and complexity; reflecting normal but non-optimal brain function) and definitely abnormal movements (DA; very limited complexity and variation including cramped synchronized movements; reflecting brain dysfunction) [START_REF] Hadders-Algra | Quality of general movements and the development of minor neurological dysfunction at toddler and school age[END_REF] (b) by applying a fine-graded 10point Likert-scale, in which score 1 denotes a virtual absence of GM-complexity and 10 a very abundant GM-complexity [START_REF] Groen | General movements in early infancy predict neuromotor development at 9 to 12 years of age[END_REF]. Scores of NO movements ranged from 10 to 8, those of NS movements from 7 to 6, those of MA movements from 5 to 4 and those of DA movements from 3 to 1. The Likert-scale fine grading has been applied before; it helps to describe movement quality but it does not assist improvement of prediction of developmental outcome compared to that based on the four classes [START_REF] Groen | General movements in early infancy predict neuromotor development at 9 to 12 years of age[END_REF]. (c) GMA ratings were dichotomized into the clinically relevant DA category (1-3 on the Likert scale; high predictive value for CP) versus a non-DA category (MA, NS, NO, i.e. 4-10 on the Likert scale) as previously described by Hadders-Algra et al. [START_REF] Hadders-Algra | Putative neural substrate of normal and abnormal general movements[END_REF]. Second, the degree of FMs is assessed: continuously present, intermittently present, sporadic and absent [START_REF] Einspieler | Prechtl's Method on the Qualitative Assessment of General Movements in Preterm, Term and Young Infants[END_REF]. Continuously and intermittently present FMs were considered as typical FMs. In the calculation of predictive values FMs were categorized as absent or present, i.e., sporadic or typical. [START_REF] Hamer | Assessment of specific characteristics of abnormal general movements: does it enhance the prediction of cerebral palsy?[END_REF].

The senior author assessed all SMIL motion videos in random order (both in terms of GMcomplexity and in terms of presence of FMs), followed by the assessment of all RGB videos in random order. The senior author is a GMA expert with more than 25 years of experience in clinical and scientific GMA rating (MH-A). She was masked for the infant's medical history and outcome.

Statistical analysis

Descriptive statistics were used to describe the infants' clinical information on single subject level (gestational age at birth, APGAR, umbilical cord pH and base excess, age at GMA recording, neonatal morbidity diagnoses, brain ultrasound findings, results of neurodevelopmental outcome at ≥12 months, GMA scores).

For the assessment of both videos by the same GMA rater, statistical tests representing testretest and intra-rater reliability measures were used. The Intraclass Correlation Coefficient (ICC; two way mixed, absolute agreement) was used to evaluate the degree of agreement between the two GM-ratings of GM-complexity using the 1-10 Likert scale and the FM-ratings.

Reliability is considered high when ICC is 0.70 or higher. The ICC values are presented with 95% confidence intervals (CI). In addition, we used Bland-Altman plots to report the agreement between the GM-complexity on the basis of both videos. Kappa statistics were used to describe the agreement of DA and Not-DA as well as for the classification of absent and present FMs of both types of video. Kappa-values of 0.41-0.60 are considered a "moderate agreement", 0.61-0.80 a "substantial agreement" and values of 0.81-1.00 as "almost perfect agreement". Two-by-two tables were used to describe the consistency of GMA for the conventional videos compared to those based on the SMIL motion video with respect to neurodevelopmental long-term outcome (CP present or absent). In addition, specificity, sensitivity, negative predictive value, and positive predictive value were calculated.

Results

Twenty-nine infants met the inclusion criteria. Thus, 2x29 sets of videos were assessed. The infants' mean corrected age at recording was 14.8 weeks (95%CI 14.1;15.5). More than half (18/29, 62%) of the infants was born before 32 weeks of gestation. All infants born at higher gestational ages were highrisk due to perinatal or postnatal complications (e.g. term asphyxia or meningitis) requiring standardised neurodevelopmental follow up examinations. At neurodevelopmental follow-up between 12-31 months CA 10 children (34%) showed a typical development; 14 had a global developmental delay (48%) and five (17%) had an ascertained diagnosis of CP (four a bilateral spastic CP, one a unilateral spastic CP). Detailed clinical characteristics of the infants are provided in Table 1 and Supplementary Table S1.

GMA rating: conventional video versus SMIL motion video

On the basis of GM-complexity assessments six infants were rated as DA, both in the conventional videos and in the SMIL motion video rating, 18 (13 SMIL motion video) as MA, and 6 (10) as NS. In both video ratings no infant was rated as NO (Table 1).

Using the dichotomy DA versus non-DA GM-complexity, 25/29 (86%) of both ratings were identical. The corresponding inter-rating agreement measured with the Cohen's Kappa was 0.58 (p = 0.002), meaning "moderate" agreement. For FMs present versus absent Cohen's Kappa was 0.78 (p = 0.001), meaning "substantial" agreement.

The GM-complexity ratings of both video methods using the 1-10 Likert scale were identical for 16/29 (55%) infants. An additional 10/29 (34%) ratings showed only a +/-1 difference between both video-methods. The ratings of three infants (10%) differed more than one point.

In two infants the ratings differed by two points (#6: coventional GMA rating of 5, corresponding to MA GM-complexity, and a SMIL motion video GMA rating of 3, corresponding to DA GMcomplexity; #15 conventional GMA rating of 3 and a SMIL motion video GMA rating of 5). In the third infant the ratings differed by three points (#3, conventional GM-complexity 5 versus SMIL motion video GM-complexity 2) (Table 2, Bland-Altman-Plot see Supplementary Figure S1). The mean Intraclass Correlation Coefficient (ICC) of both GM-complexity ratings was excellent 0.874 (95% CI 0.730;0.941).

In the FM rating of the conventional video FM's were absent in three infants (#1, #2, #6) of which two infants had DA GM-complexity and one MA GM-complexity. All three were later diagnosed with CP. In the SMIL motion video also three infants were rated as absent FM (#1, #6, #8) of which again two infants were rated having DA GM-complexity and one as having MA GM-complexity. Two of these were diagnosed with CP and one with global developmental delay at follow up. ICC of FM ratings was 0.926 (95% CI 0.843;0.965).

One infant who was diagnosed with bilateral CP (#5) and the infant with unilateral CP (#20) had presented either with sporadic or typical FMs in both videos (Table 1).

Prediction of developmental outcome

Table 2 summarizes the predictive values of the various GMA ratings including positive and negative predictive values. Sensitivity and specificity of DA vs non-DA rating of GM-complexity on the basis of the conventional videos to predict CP were 0.600 and 0.875, respectively; those of the corresponding SMIL motion videos 0.800 and 0.917, respectively. The difference between the two is illustrated by ROC curves (Supplementary Figure S2). Sensitivity and specificity of absent FMs of the conventional video were 0.600 and 1.000, those of the SMIL motion videos 0.400 and 1.000, respectively. Finally, the combination of GM-complexity and FM ratings resulted in a sensitivity and specificity of both types of video of 0.400 and 1.000, respectively. Child #20, later diagnosed with unilateral CP, was consistently missed: she showed MA GMs with FMs.

Discussion

Our clinical case-study indicated that GMA based on computer generated virtual 3D infant body models (SMIL motion videos) closely corresponds to the established gold standard based on conventional RGB videos. The SMIL motion videos were able to catch movement complexity and FMs. In addition, our data suggest that GMA ratings based on SMIL motion videos result in a similar prediction of CP as GMA based on conventional videos.

Comparison of GMA based on conventional versus SMIL motion videos

Several studies have reported excellent inter-observer agreement of skilled observers for conventional GMA [START_REF] Hadders-Algra | Quality of general movements and the development of minor neurological dysfunction at toddler and school age[END_REF][START_REF] Hadders-Algra | Quality of general movements in infancy is related to neurological dysfunction, ADHD, and aggressive behaviour[END_REF][START_REF] Fjortoft | Inter-observer reliability of the "Assessment of Motor Repertoire--3 to 5 Months" based on video recordings of infants[END_REF]. Our comparison of the 29 simultaneously recorded video pairs could be considered a similar type of test-retest analysis. Corresponding to literature data, we found substantial inter-observer reliability (dichotomous FMs and GM-complexity Cohen's Kappa 0.78 and 0.58 respectively, finer graded ratings ICC 0.926 and 0.874 respectively).

The predictive values of both ratings of GM-complexity were comparable to those described in the literature; those of FMs were somewhat lower than the values reported in the literature, where summary estimates of sensitivity and specificity of GMA based on FM assessment have been reported to be 98% [95% CI: 74;100] and 91% [95% CI: 83;93%] respectively [START_REF] Bosanquet | A systematic review of tests to predict cerebral palsy in young children[END_REF].

Interestingly, the predictive values based on the SMIL motion video were not inferior to those based on the conventional videos. While GM-complexity rating seemed to be superior using the SMIL motion video (Sensitivity 0.8 versus 0.6; PPV 0.667 versus 0.5, NPV 0.957 versus 0.913), the conventional video seemed to be slightly superior regarding the assessment of FMs (Sensitivity 0.6 versus 0.4; NPV 0.923 versus 0.889). It is conceivable that the SMIL motion video is superior to conventional GMA rating of GM-complexity, as the SMIL motion video forces the observer to focus on the Gestalt perception of GM-quality, whereas in the conventional video the assessor may be distracted and/or biased by additional visible details, such as naso-gastric tube, oxygen supply, monitoring cables or details of facial expression.

Regarding the assessment of FMs, however, the SMIL motion video at times contains slight pose inaccuracies due to noisy input data, which may be misinterpreted as very small amplitude FMs. This could explain that it was occasionally hard to distinguish sporadic and absent FMs on the basis of the SMIL videos due to insufficient data quality. This underpins the conclusion, that assessing both parameters (GM-complexity and FMs) is relevant for an optimal clinical prediction.

To understand the differences in the GMA ratings based on the conventional and SMIL motion videos of infants #2, #3, #6, and #15 MH-A reviewed these videos. She noticed the following for the infants (#3 and #6) whose GM-complexity was rated better on the conventional video (MA) than on the SMIL video (DA): Infant #3 (developmental delay at follow-up) showed especially some complex movements in the distal joints (with FMs), apparently these movements made a less prominent impression on the SMIL video. Infant # 6 who was later diagnosed with bilateral CP had shown on the conventional video a discrepancy in movement complexity between the upper and the lower part of the body, with the upper being better than the lower. This discrepancy was less striking on the SMIL video. Both videos were rated as absent FMs. Infant #15 (typical outcome at follow-up) had been assessed at 9 weeks CA. GMcomplexity of the conventional video was rated as DA, due to prominent stereotyped movement sequences in the proximal joints; the distal joints showed some complexity. The latter was more prominently visible during the SMIL video, which was therefore rated as MA.

Both videos were rated as sporadic FMs. It has been shown by others, that movement quality at 9 weeks CA may improve to better qualities during the following weeks [START_REF] Saether | A change in temporal organization of fidgety movements during the fidgety movement period is common among high risk infants[END_REF]. Presumably this happened also in this infant. Finally, the GMs of infant #2 (bilateral CP) were rated on both videos as DA. However, the FMs were scored as absent on the conventional video, but as sporadic on the SMIL videothe few FMs had been observed at the wrists only. The latter may also be due to the slight pose inaccuracies due to noisy input data of the SMIL Model suggesting the presence of sporadic FM. The differences in scores between conventional and SMIL videos could be due to such technical matters, but they also underline that GMA based on Gestalt perception may remain difficult even after years of experience; it stresses the need of automatic movement analysis following strict algorithms.

GMA was especially adequate in the prediction of bilateral CP. Yet, it missed the child who was diagnosed with unilateral CP. It has been reported before that GMA does not detect all children with unilateral CP [START_REF] Cioni | Early neurological signs in preterm infants with unilateral intraparenchymal echodensity[END_REF][START_REF] Guzzetta | General movements detect early signs of hemiplegia in term infants with neonatal cerebral infarction[END_REF][START_REF] Bouwstra | Predictive value of definitely abnormal general movements in the general population[END_REF]. The differential ability of GMA to predict bilateral and unilateral CP corresponds to the putative neural substrate of the quality of GMs. GM-quality is considered to reflect the integrity of extensive neural networks involving not only cortical areas, but also their connectivity with subcortical relay stations [START_REF] Hadders-Algra | Early human brain development: Starring the subplate[END_REF]. Indeed, evidence is accumulating that DA GMs are especially related to damage of the periventricular white matter [START_REF] Hadders-Algra | Early human brain development: Starring the subplate[END_REF][START_REF] Peyton | White Matter Injury and General Movements in High-Risk Preterm Infants[END_REF]. In part of the children with unilateral CP the lesion is restricted to the cortical grey or does only affect part of the cross-roads running through the periventricular white matter (as in infant #20) and does not massively disrupt white matter connectivity, therewith not producing DA GMs.

The detection of children with unilateral CP is also challenging due to the finding that fidgety movements are frequently present.

Two children showed DA GMs (#4 and #7) but were not diagnosed with CP. They both presented with "global developmental delay" at follow-up. Recent data indicate that the limitations in the infant's early motor repertoire do not only predict an increased risk of CP, but also an increased risk of cognitive impairment irrespective of the GMA method used for classification [START_REF] Butcher | The quality of preterm infants' spontaneous movements: an early indicator of intelligence and behaviour at school age[END_REF][START_REF] Heineman | Motor development in infancy is related to cognitive function at 4 years of age[END_REF][START_REF] Einspieler | The General Movement Assessment Helps Us to Identify Preterm Infants at Risk for Cognitive Dysfunction[END_REF].

Strengths and limitations:

The strength of the current study is that it is the first that compares GMA based on videos of computed 3D infant body models to GMA rating of conventional videos. Another strength is that the videos were rated by one masked and highly experienced GMA assessor using multiple grading strategies including explicit assessment of FMs.

The study also has limitations: the sample size was small and consisted of high risk infants only. However, this convenience sample might be considered as representative of many infant follow-up clinics and represented infants with normal, MA and DA GMs. It may also be considered that the duration of follow-up was limited, which may mean that some children with a developmental diagnosis may have been missed [START_REF] Groen | General movements in early infancy predict neuromotor development at 9 to 12 years of age[END_REF].

Current status and future perspective of GMA based on SMIL modelling

In contrast to previous approaches that capture a limited number of body joint positions based on 2D-video [27-29, 51, 52] or relying on marker based sensors attached to the baby [START_REF] Meinecke | Movement analysis in the early detection of newborns at risk for developing spasticity due to infantile cerebral palsy[END_REF][START_REF] Heinze | Movement analysis by accelerometry of newborns and infants for the early detection of movement disorders due to infantile cerebral palsy[END_REF][START_REF] Gao | Towards Reliable, Automated General Movement Assessment for Perinatal Stroke Screening in Infants UsingWearable Accelerometers[END_REF][START_REF] Karch | Kinematic assessment of stereotypy in spontaneous movements in infants[END_REF], our method allows the extraction of angles of all body joints (3 degrees of freedom per joint), 3D positions of body joints and of arbitrary points on the body surface without the need of potentially distracting markers. Our method is robust to self-occlusions (e.g. hands and feet occluding large parts of the body) and fast motion. It has been proven to display the true whole body movements of the recorded infant in 98.8% of recorded video sequences, and to have a metric accuracy, i.e. the average distance of RGB-D scan points to the model surface, of 2.51 mm (SD 0.21 mm) [START_REF] Hesse | Learning and Tracking the 3D Body Shape of Freely Moving Infants from RGB-D sequences[END_REF].

In order to further improve the SMIL tracking, we plan to integrate means to detect and fix the rare occurrence of failure cases, as recently published by Aristidou et al. [START_REF] Aristidou | Self-similarity Analysis for Motion Capture Cleaning[END_REF]. In addition, we aim to apply the SMIL motion video in a larger patient population, not only to improve the detection of predetermined motion parameters (e.g. fidgety general movements, centroid of motion, cramped synchronized activity, or a reduction of segmental movements), but also to use machine learning algorithms to quantify the motion parameters which are assumed to drive the "Gestalt perception".

Concluding remarks

Our case study of 29 high risk infants demonstrated that the amount of motion details captured by the SMIL motion video -based on a low-cost Kinect recording and the KineMAT toolenables accurate GMA at fidgety age. This implies that the SMIL motion video adequately catches the movement characteristics needed for GMA of infants with movements ranging from a normal to a definitely abnormal quality, turning it into an attractive tool for automatic GMA. Legend Tabel 2a-c: * including infant #20 later diagnosed with unilateral spastic CP. Abbreviations: DA compl. = definitely abnormal complexity GM rating (Likert Scale 1-3); Not-DA = all other GM ratings (mildly abnormal, normal suboptimal; Likert scale 4-7). FMs absent = fidgety movments scored "0", FMs present = all other fidgety movements (sporadic, intermittend, continuously). [START_REF] Hadders-Algra | Evaluation of motor function in young infants by means of the assessment of general movements: a review[END_REF][START_REF] Groen | General movements in early infancy predict neuromotor development at 9 to 12 years of age[END_REF], SMIL Skinned Multi-Infant Linear Body Model [START_REF] Hesse | Learning and Tracking the 3D Body Shape of Freely Moving Infants from RGB-D sequences[END_REF].

Supplementary Table S1

  Abbreviations:

  Infant and Toddler Development (BSITD, Bayley III Scales, full version). The local ethics committee gave clearance for this study prior to the inclusion of the first patient (Ethikkommission LMU, Project Nr. 454-16). Parents gave written informed consent prior to study entry. No family withdrew consent during the study period.

  Statistics were performed with IBM SPSS, version 25.0.0.1 (IBM Corporation USA). ROC curves were used to graphically illustrate the relationship between sensitivity and the occurrence of false positive values for DA versus non-DA GM-complexity ratings of both types of video.

Figure 1 : Setup 1 2 Figure 2 :

 122 Figure 1: Setup 1

Legend:

  Abbreviations: sex = male (m), female (f), GA = gestational age, CA = corrected age; BW = bodyweight, HIE = Hypoxic ischaemic encephalopathy, PVL = periventricular leucomalasia, PVC = periventricular cyst, CP = cerebral palsy, DD = developmental delay, either based on performance on the Bayley Scales of Infant and Toddler Development (BSITD, Version 3) or on the clinical examination mo = months. Definition of CP in accordance with international expert reccomendations.[38]; terminology of the Surveillance of Cerebral Palsy in Europe (SCPE; [56].

Table 1 : GMA Conventional video versus SMIL motion video 1

 1

		Pat ID	sex GA	CA	Clinical Diagnosis at 12-30 mo CA	GM-complexity Likert score Conventional	Fidgety movements Conventional video	GM-complexity Likert score SMIL	Fidgety movements SMIL motion video
						[38]	video		motion
									video
		1	M 29+4 12 Bilateral CP 2	0	2
		2	M 40+0 17 Bilateral CP 2	0	2
		3	F	28+4 12 Dev. Delay	5	2	2
		4	M 29+1 18 Dev. Delay	3	2	3
		5	M 40+2 9	Bilateral CP 3	1	3
		6	F	32+2 14 Bilateral CP 5	0	3
		7	F	24+5 12 Dev. Delay	3	2	4
		8	M 24+0 13 Dev. Delay	4	2	4
		9	M 35+2 18 Age adequate 4	2	4
		10	F	28+0 15 Dev. Delay	4	2	4
		11	M 40+6 17 Age adequate 4	2	4
		12	M 25+5 14 Dev. Delay	4	2	4
		13	F	28+5 16 Age adequate 4	2	4
		14	F	28+0 15 Dev. Delay	5	2	4
		15	M 41+5 9	Age adequate 3	1	5
		16	M 27+1 14 Dev. Delay	5	2	4
		17	M 31+1 17 Age adequate 5	2	5
		18	M 29+1 13 Age adequate 5	2	5
		19	F	33+4 12 Age adequate 5	2	6
		20	F	28+0 15 Unilateral CP 5	2	6
		21	F	28+5 16 Age adequate 5	2	6
		22	M 29+1 13 Age adequate 5	2	6
		23	M 23+6 15 Dev. Delay	5	2	6
		24	F	29+4 16 Dev. Delay	6	2	5
		25	M 29+4 16 Dev. Delay	6	2	6
		26	F	36+3 12 Dev. Delay	7	2	6
		27	F	26+6 17 Age adequate 7	2	7
		28	M 25+4 15 Dev. Delay	7	2	7
		29	M 41+1 16 Dev. Delay	7	2	7
	2	Legend: GM-complexity Likert scale ratings corrrespond to the following classifications 1-3 "definitely
	3	abnormal", 4-5 "mildly abnormal", 6-7 "normal suboptimal", 8-10 " normal optimal. Fidgety movements
	4	(FMs): 0 = absent; 1 = sporadically present; 2 = intermittently or continuously present. Abbreviations:
	5	CA = corrected age; convent = conventional; CP = cerebral palsy; definition of CP in accordance with

international expert reccomendations.

[START_REF] Boychuck | International expert recommendations of clinical features to prompt referral for diagnostic assessment of cerebral palsy[END_REF]

; terminology of the Surveillance of Cerebral Palsy in Europe

6

(SCPE;

[START_REF] Scpe | Surveillance of cerebral palsy in Europe: a collaboration of cerebral palsy surveys and registers. Surveillance of Cerebral Palsy in Europe (SCPE)[END_REF]

. DD: developmental delay, either based on performance on the Bayley Scales of Infant and

Table 2 :

 2 Prediction of CP at follow up by conventional video GMA rating and GMA rating based on SMIL motion videos.

	a) based on GM-complexity
	Cerebral palsy	No CP	Total	Q criteria
	Conventional video				Sensitivity 0.6
	DA-compl.	3	3	6	Specificity 0.875
	Not-DA	2*	21	23	PPV 0.5
	Total	5	24	29	NPV 0.913
	SMIL motion video				Sensitivity 0.8
	DA compl.	4	2	6	Specificity 0.917
	Not-DA	1*	22	23	PPV 0.667
	Total	5	24	29	NPV 0.957
	b) based on FMs
	Cerebral palsy	No CP	Total	Q criteria
	Conventional video				Sensitivity 0.6
	FMs absent	3	0	3	Specificity 1
	FMs present	2*	24	26	PPV 1
	Total	5	24	29	NPV 0.923
	SMIL motion video				Sensitivity 0.4
	FMs absent	2	0	2	Specificity 1
	FMs present	3*	24	27	PPV 1
	Total	5	24	29	NPV 0.889
	c) based on GM-complexity and FMs
		Cerebral	No CP	Total	Q criteria
		palsy
	Conventional video
	DA-compl., FMs absent	2	0	2	Sensitivity 0.4
	Not-DA, FMs absent	1	0	1	Specificity 1
	DA-compl., FMs present	1	3	4	PPV 1
	Not-DA, FMs present	1*	21	22	NPV 0.889
	Total	5	24	29
	SMIL motion video
	DA-compl, FMs absent	2	0	2	Sensitivity 0.4
	Not-DA, FMs absent	0	0	0	Specificity 1
	DA-compl, FMs present	2	2	4	PPV 1
	Not-DA, FMs present	1*	22	23	NPV 0.889
	Total	5	24	29

: Clinical characteristics of the study population

								Bayley	III:
	Pat ID 17 1 2 18 3 19 4 20 5 21 6 7 22 8 9 23 24 10 25 11 26 12 27 13 28 14 29	sex M M M M F F M F M F F F M M M M F F M M F M F F M F M	GA 31+1 29+4 12 CA 40+0 17 29+1 28+4 12 33+4 29+1 18 28+0 40+2 9 28+5 32+2 14 24+5 12 29+1 24+0 13 35+2 18 23+6 29+4 28+0 15 29+4 40+6 17 36+3 25+5 14 26+6 28+5 16 25+4 28+0 15 41+1	BW -1370 8/9/9 APGAR 1/5/10 minutes 8/7/9 3500 1/2/6 1190 8/8/9 800 8/9/9 1850 9/10/10 7.47 Umbilical cord pH 7.33 7.41 7.14 7.34 7.3 960 2/8/9 7.36 900 9/9/9 7.35 2500 6/7/7 7.08 1170 5/8/9 7.08 1810 6/8/-7.36 820 7/9/9 7.42 1320 7/8/9 7.36 620 7/8/9 7.39 2590 9/9/10 7.4 ---1756 5/6/7 7.36 480 7/8/10 7.19 850 2/2/2 0 4526 -/9/10 7.32 840 1/5/8 7.4 750 1/2/4 7.31 890 6/8/10 7.42 1300 9/9/10 7.47 655 4/9/9 7.4 650 8/10/10 7.38 4280 1/3/5 6.79	Umbilical Base-excess -1 -2 -12 -3.2 -8 -2.5 1 -5.5 -11 -10.3 0 2 -3 1 -2 --4.9 -5.5 0 0 0 -7 -0.7 -4.5 -2.1 -6 -27.2	Cerebral ultrasoun d Unspecific Abnormal (PVL) Normal Abnormal (HIE term asphyxia) Unspecific Normal Normal Abnormal (PVC left) Abnormal (HIE term Normal asphyxia) Abnormal (PVL) Normal Unspecific Unspecific Abnormal (ICH I right, II° left) Unspecific Normal Normal Normal Unspecific Normal Unspecific Unspecific Unspecific Normal Unspecific Unspecific	Clinical typical development Diagnosis at at 26 mo CA 12-30 months Bayley III: Bilateral CP typical development at 24 mo CA Bilateral CP Typical development Bayley III: DD at at 12 mo CA 23 mo CA Clinically DD at Unilateral CP at 6 mo CA 18 mo CA Bayley III: Bilateral CP typical development at 28 mo CA Bilateral CP Bayley III: Bayley III: DD at typical developmentat 24 mo CA 24 mo CA Clinically DD at 12 mo CA Bayley III: DD at 31 mo CA Typical development Clinically DD at 12 mo CA at 12 mo CA Bayley III: DD at Clinically DD at 12 mo CA 26 mo CA Typical Clinically DD 12 mo CA development Bayley III: at 12 mo CA Clinically DD typical development at12 mo CA at 25 mo CA Bayley III: typical Clinically DD at 12 mo CA development at 28 mo CA 24 mo CA Bayley III: DD at Clinically DD at 12 mo CA
								Bayley	III:
	15	M	41+5 9	3450 3/6/10	7.23	-8.4	Normal	typical development
								at 24 mo CA
	16	M	27+1 14	1100 4/9/9	7.31	-4.3	Normal	Bayley III: DDat 30 mo CA

Supplementary Figure S1: Bland-Altman Plot: 1-10 Likert scale rating conventional GMA versus SMIL motion video rating. S2a,b

Supplementary Figure

Legends to the figures

Figure 1 Study set-up

We recorded General Movements of Infants in the clinical setting of an outpatient clinic. We correlated expert GMA ratings of standard RGB videos with GMA ratings on SMIL motion videos of the same sequence. SMIL motion videos are the result of capturing 3D shape and motion with the SMIL model [START_REF] Hesse | Learning and Tracking the 3D Body Shape of Freely Moving Infants from RGB-D sequences[END_REF]. (further explanation see: https://www.youtube.com/watch?v=aahF1xGurmM&feature=youtu.be) The upper panel shows 8 representative poses of a three minute video recording of an infant at fidgety age. The infant presents with typical motion features of complexity and variation during active wakefulness; his GMA complexity was rated as normal suboptimal (Likert-score 7). The lower panel are the corresponding SMIL motion video frames; also in these panels GM-complexity can be clearly observed. Note that fidgety movements can not be shown in the static poses of the figure.

Supplementary Figure S1: Bland-Altman Plot of correspondence of Likert-scale rating of GM-complexity comparing conventional RGB video and SMIL motion video.

The Bland-Altman plot depicts the agreement between the ratings of both videos. The Y axis shows the difference between the two paired measurements, the X-axis represents the average of these measures. Mean value of the 29 GMA rating differences = 0.034 (grey solid line). Standard deviation of mean value: ±0.981). Upper dotted line = upper reference limit (Mean value + 1.96 x Stddev.)= 1.958. Lower dotted line = lower reference limit (Mean value -1.96 x Stddev) = -1.888. Regarding the three outliers infant #3 (developmental delay) infant #6 (bilateral spastic CP) and infant #15 refer to the discussion section.

Supplementary Figure S2: ROC of sensitivity of GMA DA complexity rating based on the two types of video


Mehdi Bouatou

Cyril Chacon

Frédéric Joucken

Yann Girard

Vincent Repain

Amandine Bellec

Sylvie Rousset

Robert Sporken

Cesar González

Yannick J Dappe

Jérôme Lagoute
email: jerome.lagoute@univ-paris-diderot.fr

Control of Dipolar Switches on Graphene by a Local Electric Field

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

Introduction

Molecular switches can toggle between different stable states under external excitation. They play a key role in biology due to their ability to sense their environment. [START_REF]Molecular Switches[END_REF] They have been intensively studied as building blocks to realize basic functions in molecular electronics. 2 Most molecular switches rely on stereoisomerization, [START_REF] Henzl | Reversiblecistrans Isomerization of a Single Azobenzene Molecule[END_REF][START_REF] Comstock | Reversible Photomechanical Switching of Individual Engineered Molecules at a Metallic Surface[END_REF] tautomerization [START_REF] Liljeroth | Current-induced hydrogen tautomerization and conductance switching of naphthalocyanine molecules[END_REF][START_REF] Auwärter | A surface-anchored molecular four-level conductance switch based on single proton transfer[END_REF][START_REF] Harsh | Controlling Hydrogen-Transfer Rate in Molecules on Graphene by Tunable Molecular Orbital Levels[END_REF] or electrocyclization. [START_REF] Reecht | Disentangling electron-and electric field-induced ring-closing reactions in a diarylethene derivative on Ag(111)[END_REF] Macrocyclic compounds from the families of porphyrins and phthalocynanines have been particularly investigated in that context. Shuttlecock shaped lead phthalocyanine (PbPc) and tin phthalocyanine (SnPc) are characterized by a non planar structure with the central metal atom protruding out of the molecular plane leading to two stable states with the metal atom pointing on one side or the other of the molecule. These two conformations are identical in gas phase but are uneven on a substrate where the metal atom can point toward the vacuum region (up state) or the substrate (down state).

The charge distribution in these molecules induces a permanent electric dipole perpendicular to the molecular plane. [START_REF] Toader | Tuning the energy level alignment at the SnPc/Ag (111) interface using an STM tip[END_REF] The switching of these molecules can be activated at the single molecule level with a scanning tunneling microscope (STM). [START_REF] Wang | Pushing and pulling a Sn ion through an adsorbed phthalocyanine molecule[END_REF][START_REF] Nacci | Reversible switching of single tin phthalocyanine molecules on the InAs(111)A surface[END_REF][START_REF] Forker | Fraternal twins: distinction between PbPc and SnPc by their switching behaviour in a scanning tunnelling microscope[END_REF] For PbPc, the two conformers are spontaneously present on a substrate, but no tip-induced switching could be achieved. [START_REF] Forker | Fraternal twins: distinction between PbPc and SnPc by their switching behaviour in a scanning tunnelling microscope[END_REF][START_REF] Sperl | Electronic superstructure of lead phthalocyanine on lead islands[END_REF][START_REF] Nguyen | Noninvasive coupling of PbPc monolayers to epitaxial graphene on SiC[END_REF] For SnPc on Ag(111), only an irreversible switching from up to down conformers could be induced. [START_REF] Wang | Pushing and pulling a Sn ion through an adsorbed phthalocyanine molecule[END_REF] A reversible switching has been achieved using a decoupling layer of SnPc 10 or Perylenetetracarboxylic dianhydride (PTCDA). [START_REF] Forker | Fraternal twins: distinction between PbPc and SnPc by their switching behaviour in a scanning tunnelling microscope[END_REF] C. Nacci et al. have shown that single SnPc molecules can be reversibly switched when they are adsorbed on a semiconducting substrate of InAs(111). [START_REF] Nacci | Reversible switching of single tin phthalocyanine molecules on the InAs(111)A surface[END_REF] The activation process of molecular switches by STM can involve an inelastic excitation eventually combined with an electric field [START_REF] Auwärter | A surface-anchored molecular four-level conductance switch based on single proton transfer[END_REF][START_REF] Nacci | Reversible switching of single tin phthalocyanine molecules on the InAs(111)A surface[END_REF][START_REF] Kumagai | Thermally and Vibrationally Induced Tautomerization of Single Porphycene Molecules on a Cu(110) Surface[END_REF][START_REF] Kügel | Analyzing and Tuning the Energetic Landscape of H 2 Pc Tautomerization[END_REF] or resonant tunneling. 2,[START_REF] Harsh | Controlling Hydrogen-Transfer Rate in Molecules on Graphene by Tunable Molecular Orbital Levels[END_REF][START_REF] Reecht | Disentangling electron-and electric field-induced ring-closing reactions in a diarylethene derivative on Ag(111)[END_REF][START_REF] Wang | Pushing and pulling a Sn ion through an adsorbed phthalocyanine molecule[END_REF][START_REF] Schaffert | Imaging the dynamics of individually adsorbed molecules[END_REF][START_REF] Wirth | Diarylethene molecules on a Ag (111) surface: stability and electron-induced switching[END_REF][START_REF] Krönlein | Energetic and Spatial Mapping of Resonant Electronic Excitations[END_REF] The local electric field in a STM junction has been employed in previous works to achieve molecular manipulation. [START_REF] Whitman | Manipulation of adsorbed atoms and creation of new structures on room-temperature surfaces with a scanning tunneling microscope[END_REF][START_REF] Bouju | Electric field effect and atomic manipulation process with the probe tip of a scanning tunneling microscope[END_REF][START_REF] Alemani | Electric field-induced isomerization of azobenzene by STM[END_REF][START_REF] Pawlak | Design and characterization of an electrically powered single molecule on gold[END_REF][START_REF] Nagaoka | Controlling molecular condensation/diffusion of copper phthalocyanine by local electric field induced with scanning tunneling microscope tip[END_REF][START_REF] Lu | Modification of the Potential Landscape of Molecular Rotors on Au(111) by the Presence of an STM Tip[END_REF] In the case of SnPc, the existence of a permanent electric dipole opens the opportunity to use an electric field to control the switching yield and direction, but the proper molecule-substrate combination has to be found to observe and investigate this mechanism. In particular it is expected to invert the switching direction by reversing the bias polarity. In previous studies, for second layer SnPc molecules, a reversible switching was observed at positive bias without evidence of dipolar interaction. [START_REF] Wang | Pushing and pulling a Sn ion through an adsorbed phthalocyanine molecule[END_REF] On PTCDA, an up to down conversion was measured at negative bias, whereas a down to up conversion was eventually observed at positive bias but suffered from a lack of reproducibility. [START_REF] Forker | Fraternal twins: distinction between PbPc and SnPc by their switching behaviour in a scanning tunnelling microscope[END_REF] In the case of InAs substrate, an up to down transition was induced at negative bias, while a positive bias led to a down to up transition accompanied by a lateral displacement. [START_REF] Nacci | Reversible switching of single tin phthalocyanine molecules on the InAs(111)A surface[END_REF] With the recent advent of low dimensional materials, there has been a rise of interest for combining molecules with two dimensional materials. [START_REF] Kuila | Chemical functionalization of graphene and its applications[END_REF][START_REF] Hong | Recent progress in organic molecule/graphene interfaces[END_REF][START_REF] Garnica | Long-range magnetic order in a purely organic 2D layer adsorbed on epitaxial graphene[END_REF][START_REF] Riss | Imaging and tuning molecular levels at the surface of a gated graphene device[END_REF][START_REF] Pham | Electronic Interaction between Nitrogen-Doped Graphene and Porphyrin Molecules[END_REF][START_REF] Wickenburg | Tuning charge and correlation effects for a single molecule on a graphene device[END_REF][START_REF] Pham | Selective control of molecule charge state on graphene using tip-induced electric field and nitrogen doping. npj 2[END_REF] Here we show that the combination of SnPc with graphene allows to obtain a system where the molecules in direct contact with the substrate can be reversibly switched and where the yield and direction of the switching is controlled by the interaction of the electric field with the molecular dipole. We also emphasize the specificity of the graphene substrate by comparing to the case of a Au(111) substrate where the switching is irreversible due to a quenching of the molecular dipole.

In addition we show that data storage can be achieved with that system. Indeed, SnPc molecular lattice has been proposed as a suitable candidate to store binary information in idealized experimental conditions. 9 However a reliable binary data storage requires a periodic array and a controllable reversible switching with different writing parameters for the two bit values. These conditions have not been reached in the above mentioned molecular systems and are fulfilled on graphene.

Methods

Multilayer (≈5-10) graphene samples were obtained on SiC(000 1) by annealing the substrates in ultrahigh vacuum (UHV) at 1320 • C for 12 min under a silicon flux of ≈1ML/min. After synthesis, the sample was transferred in air to a UHV chamber and degassed for a few minutes before the measurements. SnPc molecules (TCI Chemical) were sublimated using performed with a low-temperature STM apparatus (Omicron) working at 4.6 K at a pressure lower than 1 × 10 -10 mbar. The dI/dV spectra were acquired using a lock-in detector at a frequency of ca. 823 Hz and a modulation amplitude of 35 mV. The measurements were performed with an electrochemically etched tungsten tip. Before measuring on graphene, the tip was calibrated on a Au(111) surface until it shows the Shockley surface state feature in the spectroscopic measurements.

Density Functional Theory (DFT) calculations have been performed in order to characterize accurately the SnPc molecule on graphene in support of the experimental results. Hence, we have used the very efficient localized-orbital basis set DFT code Fireball [START_REF] Lewis | Advances and applications in the FIREBALL ab initio tight-binding molecular-dynamics formalism[END_REF] to optimize the molecular geometries, determine the total energy of the system and the electronic densities.

In particular we have modelled the molecular network on graphene, considering a 9×9 square cell of graphene with four SnPc molecules on top, before optimizing it until the forces went lower than 0.1 eV/ Å. Also, we have determined the electronic properties of the single SnPc molecule on a 15×15 hexagonal cell of graphene, by optimizing two molecular structures, one with the Sn atom in up position and the other with the Sn atom in down position.

We have consequently determined the corresponding electrostatic dipoles from the atomic positions and electronic charges. By varying rigidly the position of the Sn atom between the down and up states, we have determined the energy barrier of molecular switching, as well as the intermediate dipoles along the switching process. Finally, once we have obtained the atomic and electronic structure with Fireball, we use a standard W tip within the nonequilibrium Keldysh Green function formalism to estimate the tunneling current for the STM simulations. [START_REF] González | Theoretical study of carbon-based tips for scanning tunnelling microscopy[END_REF][START_REF] Dubey | Weakly Trapped, Charged, and Free Excitons in Single-Layer MoS2 in the Presence of Defects, Strain, and Charged Impurities[END_REF]

Results and Discussion

On a Au(111) substrate, single SnPc molecules exhibit two types of topographical signatures with a node or a maximum in the center (Fig 1a) which are attributed to down and up conformations respectively. The up configuration is more frequent (we observed 86% of molecules in the up state on a sample of 370 molecules). This distribution is supported by our ab initio calculations that give a total energy of the up molecule that is 0.47 eV below the total energy of the down molecule on Au(111). These two configurations are better distinguished in the conductance maps (Fig. 1(b-c)) that reflect the downshift in the electronic spectrum of the molecules measured by dI/dV spectroscopy (Fig. 1d). A conductance map at negative bias also reveal a contrast between up and down configurations (supplementary information Fig. S1). For the up conformation two resonances are measured at 1.1 V and -1 V. When measuring the spectrum from positive to negative bias, an abrupt decrease is observed around -1.5 V. The voltage of the jump was measured between -1.5 V and -1.7 V on different molecules. This jump corresponds to the switch of the SnPc molecule from up to down. In all our attempts it was not possible to switch back the molecule from down to up, using a sample bias voltage ranging between -4 V and +4 V. The spectroscopy of the down state reveals a resonance at +0.8 V, although the features are less pronounced on that conformation, possibly because of a stronger interaction with the substrate, which corresponds to the LUMO of the molecules. The only difference comes from the intensity of the central protusion due to the position of the Sn atom. The irreversibility of the switching is similar on Au( 111) to what has been reported on Ag(111). [START_REF] Wang | Pushing and pulling a Sn ion through an adsorbed phthalocyanine molecule[END_REF] On graphene, the low molecule-surface interaction prevents single molecule imaging as previously reported for other molecules. [START_REF] Pham | Electronic Interaction between Nitrogen-Doped Graphene and Porphyrin Molecules[END_REF][START_REF] Pham | Selective control of molecule charge state on graphene using tip-induced electric field and nitrogen doping. npj 2[END_REF] Therefore, after molecule deposition on graphene at low temperature, the sample was annealed to room temperature to allow for diffusion and subsequently cooled down for measurements. As a result, the molecules arrange in a nearly square lattice with unit cell vectors a = 1.34 ± 0.01 nm , b = 1.31 ± 0.02 nm at an angle of 91 ± 1 • (Fig. 2a). This lattice corresponds to the lowest energy structure obtained by optimizing the molecular structure on graphene using Density Functional Theory (DFT) calculations (see Methods for details) as shown in Fig. 2b. Experimentally, we find that all molecules are naturally in the up state. This is due to a higher stability of the up state for which the total energy is lower by 0.94 eV as revealed by our ab initio calculations performed on the two configurations shown in Fig. 2c. Moreover, the adsorption energy is -3.2 eV for a molecule in the up state and -1.2 eV for the down state (see supplementary information Fi. S2). Our calculations also reveal that the SnPc is acting as an electron acceptor with a fractional negative charge of 0.038 electron for the up state and 0.048 electron for the down state. Inside the molecule, the Sn atom has an extra negative charge of 0.59 electron for the up state and 0.64 electron for the down state. In gas phase, the extra charge of the Sn atom is found to be 0.61 electron, therefore the charge of the Sn atom changes by -0.02 electron for the up state and +0.03 electron for the down state. The increase of the Sn charge for the down state can be due to the stronger molecule-graphene interaction. Due to the nonhomogeneous distribution of the charge in the molecule, the SnPc exhibits a dipole of 2.33 D for the up state and 4.27 D for the down state with a contribution in the z direction of 2.14 D for up and -3.75 for down. In the gas phase, the calculated dipole is 2.76 D in the z direction. In contrast to the case of metallic substrates, it is possible to reversibly switch the molecules between the two states with the STM tip on graphene as shown in Fig. 2d.

With the STM tip above a molecule, a voltage sweep between 3 V and -3 V leads to two subsequent switching at negative and positive bias. When ramping the bias voltage from 3 V to -3 V, a jump appears in the I(V) curve at negative bias corresponding to a switch of the molecule from the up to the down state. reversing the ramping a jump appears at positive bias corresponding to a down to up transition. Therefore, the I(V) curve exhibits a hysteresis loop indicating a remarkable dependence of the switching direction with the voltage polarity. Successive images taken before and after voltage ramping show the the two types of switching events (Fig. 2e). To unambiguously identify the up and down conformers in the topographical images, we have calculated STM images from the optimized configurations obtained in DFT (see Methods). The images show that the up state exhibits a bright spot in the center of the molecule while the down state exhibits a depletion in the center of the molecule, in full agreement with the experimental images (Fig. 2f). We also measured the electronic spectrum of the two states by dI/dV spectroscopy revealing a downshift of the molecular states for the down configuration as compared to the up state (Fig. 2g) similar to what has been measured on Au(111) (Fig. 1d). The I(V) curves obtained during the voltage sweep show that the switching of the molecules occurs only in one direction at a given voltage polarity. However, when the tunneling conditions are optimized, by approaching the STM tip and measuring on long timescale, we observed that it is possible to reverse the switching at both polarities as evidenced by the emergence of a telegraph signal in the tunneling current. Figure 3a shows the signal measured for sample bias voltages of 2.4 V and -2.3 V. The lifetime of up and down state are clearly different, a higher lifetime is observed for the up (down) state at positive (negative) bias voltage. The difference of lifetime is partly due to the fact that the tunneling current is different for the two levels. In order to quantify the efficiency of switching excitation independently of the value of the tunnel current, we calculated the yield corresponding to the number of switch per electron. For each plateau in the current signal we calculated the yield Y = e/Iτ where e is the electron charge, I is the mean value of the tunneling current and τ is the time spent in the state. Then we calculate mean value of the yield Y for the up or the down state. The voltage dependence of the yields is reported in Fig. 3b,c. At positive bias, the down to up yield is around two orders of magnitude larger than the up to down transition. At negative bias this trend is inverted. Note that by changing the tip height to modify the tunneling current at a fixed bias voltage, the yield shows small variations (see supplementary information Fig. S3).

The inversion of the relation between the two yields with the bias polarity is an evidence of the interplay between the molecular dipole and the electric field. In Fig. 3d we present a simple dipolar interaction picture describing the expected typical energy profile of the molecule along the switching reaction coordinates under the application of an electric field.

In the absence of an electric field, the up state has a lower energy than the down state as mentioned above. In the up state, SnPc has an electric dipole P with a positive component along the z axis (from graphene to vacuum). In the down state, the dipole is in the opposite direction. Under the application of an electric field E, a dipolar potential energy -P . E adds up to the molecule energy. When the electric field is positive (negative) along the z direction, the dipolar energy is positive (negative) for the down state and negative (positive) for the up state. This energy is larger for the down state due to a larger electrostatic dipole.

In the middle position where the metal atom is in the molecular plane, there is no dipole Energy calculated without electric field, and with positive and negative electric field. The zero energy was set to be at the bottom of the graph and correspond to a calculated total energy of -76510 eV.

E = 2 V/Å E = -2 V/Å E = 0 V/Å
E = 2 V/Å E = -2 V/Å down → up up → down
moment in the z direction, therefore the dipolar energy cancels. Therefore, for positive bias, the energy of the down state increases and the energy of the up state decreases, leading to a lower energy barrier for down to up transition than for the up to down transition. This explains well the higher yield for down to up transition measured experimentally (Fig. 3c).

At negative bias, the electric field and dipolar energies are reversed and the up to down transition is favored (larger yield in Fig. 3b). It is worth noticing another difference between the positive and negative electric field cases. The application of a negative electric field increases the energy of the up state and decreases the energy of the down state. Without electric field the up state has a lower energy, the application of a negative electric field can invert the situation and can lead to a up state with a higher energy than the down state.

Therefore at negative bias, an increase of the electric field amplitude could progressively induce an inversion of the switching yields relation.

At low negative electric field, a higher yield is expected for the down to up transition, while at large electric field it is the up to down transition that is expected to show a higher yield. This could explain that in Fig. 3b the yields are close around -2 V and the down to up yield becomes larger than up to down when the bias amplitude is increased. Another set of data measured on the same molecule with a larger tunnel current confirm an inversion of the yields order at -2 V (supplementary information Fig. S4). In order to test the dipolar model hypothesis, we performed further DFT calculations. Hence, we have considered 9 molecular configurations consisting in relaxed down (0) and up (8) states, and intermediate configurations with the Sn atom displaced between the two final positions (supplementary information Fig. S5). For each configuration we have computed the dipole momentum in the z direction (from the atomic position and their respective electronic charges obtained in DFT) and calculated the corresponding potential energy due to its interaction with an applied electric field of 2 V/ Å(Fig. 3e). The dipolar energy was then added to the DFT calculated potential energy in order to obtain the total energy shown in Fig. 3f. For a positive electric field the energy of the down state increases and the energy of the up state decreases. For a negative electric field, the trend is reversed. The energy variation of the down state is larger than that of the up state which is due to the larger dipole of the down state. The calculations agree well with the model described above based on the interaction of the molecular dipole with the applied electric field that drives the switching yield of the molecules. This interaction explains the crucial difference between the properties of SnPc on Au(111) and on graphene. The calculated spatial distribution of charge density shows that on Au(111) the charge density of the molecule and the substrate merge together while on graphene they are well separated (supplementary information Fig. S6). As a consequence the molecule keeps its dipole on graphene but not on Au(111) and the electric field effect measured on graphene does not occur on the metallic substrate. Finally, the hysteretic behaviour of molecule switching due to the dipolar interaction allows for a perfect control of the state of molecules in a monolayer. This allows to store binary data as demonstrated by writing an artificial pattern as shown in Fig. 4. Since all molecules are initially in the up state, the pattern was realized by switching selected molecules to the down state. The switching was done by placing the tip above selected molecules and applied a voltage pulse of -2.5 V during 100 ms. The resulting pattern is stable and no spontaneous switching was observed in the timescale of the experiment. Considering each molecule as one bit of memory with an area corresponding to the molecular lattice unit cell 1.75 nm 2 , the SnPc on graphene allows to store information with a very high density of 57 Tbit/cm 2 .

Conclusions

In conclusion, SnPc molecules on graphene were shown to form bidimensional array of molecular switch. In contrast to the case of metallic substrates where a switching can be induced by STM irreversibly from up to down state, the switching on graphene is reversible. We have shown that the electric field plays an essential role in the control of the molecular switches.

The molecules tend to switch in order to align their electric dipole with the applied electric field. As a result, I(V ) curves show a hysteresis evidencing that negative electric field (from tip to sample) favors a switching from the up state to the down state while a positive field favors a switching in the other direction. This mechanism is reproducible and the two states of molecules are stable, allowing for storing binary information as demonstrated by writing an artificial pattern in a molecular lattice.

Supporting Information Available

SnPc on Au(111). Adsorption energy of up and down states on graphene. Switching yield for different tip heights. Switching yield at negative bias voltage. Molecular conformations for calculated energies in a switching event. Spatial distribution of charge density for SnPc on Au(111) and on graphene.

Figure 1 :

 1 Figure 1: (a) STM image of SnPc on Au(111) (1 V, 200 pA). The five molecules on top are in the up state. The four molecules in the bottom part are in the down state obtained after tip induced switching. (b-c) Conductance maps at 0.8 V (b) and 1.1 V (c) of the same area as in (a). (d) dI/dV spectra of up (red) and down (black) conformations of SnPc molecules. The spectra where measured by ramping the sample bias voltage from positive to negative values. At the end of the dI/dV measurement of the up state the molecule switches to a down state at -1.53 V. The insets show the STM image of up and down states of SnPc ((1 V, 200 pA, image size 2.5 nm×2.5 nm).

Figure 2 :

 2 Figure 2: (a) STM image of a SnPc lattice on graphene measured at 1.5 V, 5 pA. All molecules are spontaneously in up state. (b) Calculated structure of the SnPc lattice on graphene in the up state. Color code for atoms: C-yellow, H-white, N-blue, Sn-red. (c) Calculated structure of SnPc in up (top) and down (bottom) states on a monolayer of graphene. (d) I(V) signal measured with the STM tip above the center of a SnPc while ramping the bias voltage from 3 V to -3 V (yellow curve) and then from -3 V to 3 V (blue curve). The arrows indicate the direction of voltage ramping. (e) Successive images (1.5 V, 10 pA) recorded on the same area in its pristine state (left), after switching the central molecule by ramping the bias voltage from +3V to -3 V (center) and switching back by ramping the bias voltage from -3 V to 3 V (right). The I(V) signal recorded during the manipulation is indicated between the corresponding images. The arrows indicate the arrows indicate the direction of the voltage sweep. (f) Calculated STM images of SnPc on graphene in the up state (left) and down state (right) at 1.5 V. (g) dI/dV spectroscopy measured at the center of a SnPc molecule in up state (yellow) and in down state (blue).

Figure 3 :

 3 Figure 3: (a) Tunnel current recorded above a SnPc molecule while applying a bias voltage 2.4 V (top) and -2.3 V (bottom). The tip-sample separation was reduced from the starting setpoint of 1.5 V, 5 pA by 0.2 Å for negative bias measurements and 0.8 Å for positive bias. The same conditions were applied for data shown in (b) and (c). (b) Switching yield for down to up (blue) and up to down (yellow) at negative bias voltages. (c) Same as (b) for positive bias voltage. (d) Schematic illustration of the role of the electric field on the switching of SnPc molecules. The blue curve corresponds to the energy profile of a molecule as function of its conformation from down to up state. When a positive electric field is applied (top part) the potential energy corresponding to the interaction of molecular dipole with the electric field is positive for the down state and negative for the up state. The resulting expected energy profile of the molecule under positive electric field is shown with the purple curve. The bottom part shows the effect of a negative electric field. (e) Dipole energy calculated for down (0), up (8) and intermediate configurations with an electric field of 2 V/ Å. The schemes show the molecular conformations for states 0, 4 and 8. (f) Calculated total energy of SnPc on graphene for molecular configurations going from down state (0) to up state (8).Energy calculated without electric field, and with positive and negative electric field. The zero energy was set to be at the bottom of the graph and correspond to a calculated total energy of -76510 eV.

Figure 4 :

 4 Figure 4: STM image of an artificial pattern written in a SnPc molecular lattice on graphene 27 nm ×10 nm (1.85 V, 3 pA).

Acknowledgement

This project has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement No. 766726.