Jean-François Landrier
email: francois.landrier@univ-amu.fr

Franck Tourniaire

Soumia Fenni

Charles Desmarchelier

Patrick Borel

Modulation of inflammation by tomatoes and lycopene in the context of cardiometabolic diseases

Keywords: AKT, protein kinase B BCO1, beta-carotene oxygenase 1 BCO2, beta-carotene oxygenase 2,

Tomato, tomato products as well as lycopene consumption are overall acknowledged to exert anti-inflammatory effects. This has been reported in numerous in vitro, animal and clinical studies, on various tissue and cell types, including adipose tissue and adipocytes, and in several physiopathological contexts, such as cardiovascular diseases, type-2 diabetes, obesity or metabolic syndrome. These anti-inflammatory effects are partly related to tomato and lycopene impact on inflammatory signaling pathways.

The goal of this review is to summarize the current knowledge on this topic and to identify gaps that remain to be filled and data that still need to be reinforced in order to definitely demonstrate the interest of tomato or lycopene consumption to blunt the inflammation associated with cardiometabolic diseases.

Introduction

Lycopene is a lipophilic pigment which is responsible for the red color of various fruits , such as tomato, water melon, guava or grapefruit. It belongs to the carotenoid family, and more specifically to the carotene class, but it is not a precursor of retinol (vitamin A). In Western diets, tomatoes and tomato products represent the major sources of lycopene [START_REF] Rao | Lycopene[END_REF].

Whereas all-trans lycopene is the main dietary form [START_REF] Richelle | The proportion of lycopene isomers in human plasma is modulated by lycopene isomer profile in the meal but not by lycopene preparation[END_REF], lycopene can be found in human blood and tissues under the all-trans form and as cis-isomers and the conformation of the molecule could modulate its activity . It is unclear whether the change in its conformation results from chemical and/or enzymatic processes but it has been observed that the percentage of cis isoforms increased following the absorption of all-trans lycopene [START_REF] Richelle | Lycopene isomerisation takes place within enterocytes during absorption in human subjects[END_REF] [START_REF] Moran | Compartmental and noncompartmental modeling of (1)(3)C-lycopene absorption, isomerization, and distribution kinetics in healthy adults[END_REF] and represented around a third of the total lycopene [START_REF] Ross | Lycopene bioavailability and metabolism in humans: an accelerator mass spectrometry study[END_REF]. The ratio between trans and cis isomers seems to vary depending on the tissues investigated: in humans, it is close to 1 in plasma and to 2 in the human liver [START_REF] Khachik | Transformations of selected carotenoids in plasma, liver, and ocular tissues of humans and in nonprimate animal models[END_REF].

Beside native lycopene, several metabolites have been described in various tissues and biological fluids. Indeed, Khachik et al. have identified a metabolite of lycopene named 2,6-cyclolycopene-1,5 diol that is present in human plasma, liver and milk [START_REF] Khachik | Partial Synthesis and Structural Elucidation of the Oxidative Metabolites of Lycopene Identified in Tomato Paste, Tomato Juice, and Human Serum[END_REF][START_REF] Khachik | Transformations of selected carotenoids in plasma, liver, and ocular tissues of humans and in nonprimate animal models[END_REF]. Studies have identified circulating and tissue lycopene metabolites (aldehydes) produced by the shortening of the isoprenoid chain of the molecule: apo-6', apo-8' and apo-12'-lycopenals have been found in the liver of rodents fed with lycopene [START_REF] Gajic | Apo-8'-lycopenal and apo-12'lycopenal are metabolic products of lycopene in rat liver[END_REF] or tomato products [START_REF] Tan | beta-Carotene-9',10'-oxygenase status modulates the impact of dietary tomato and lycopene on hepatic nuclear receptor-, stress-, and metabolism-related gene expression in mice[END_REF][START_REF] Martin-Pozuelo | The effect of tomato juice supplementation on biomarkers and gene expression related to lipid metabolism in rats with induced hepatic steatosis[END_REF]. These compounds have also been identified in human plasma following the consumption of tomato juice, together with apo-10' and apo-14'-lycopenals [START_REF] Kopec | Identification and quantification of apo-lycopenals in fruits, vegetables, and human plasma[END_REF]). However, it was not possible to find out whether they were produced endogenously from lycopene since they were also present in the tomato products.

The identification of the genetic sequences of two isoforms of carotenoid oxygenases (beta-carotene oxygenase 1 and 2, BCO1 and BCO2) in mammals has allowed the synthesis of the corresponding recombinant proteins. Studies have been undertaken to characterize their ability to cleave lycopene in vitro. BCO1 activity is located in the cytoplasm and is in charge of the first step of synthesis of retinoids (i.e. retinal) from provitamin A carotenoids [START_REF] Redmond | Identification, expression, and substrate specificity of a mammalian beta-carotene 15,15'-dioxygenase[END_REF]. Redmond et al. have reported a weak activity of recombinant mouse BCO1 towards lycopene [START_REF] Redmond | Identification, expression, and substrate specificity of a mammalian beta-carotene 15,15'-dioxygenase[END_REF]) but more recently, dela Sena et al. found that recombinant human BCO1 is able to cleave lycopene at position 15,15' almost as efficiently as beta-carotene [START_REF] Dela Sena | Substrate specificity of purified recombinant human beta-carotene 15,15'-oxygenase (BCO1)[END_REF]). BCO2 activity is restricted to mitochondrial membrane, where it prevents non provitamin A carotenoid accumulation, which can impair the respiratory chain [START_REF] Amengual | A mitochondrial enzyme degrades carotenoids and protects against oxidative stress[END_REF][START_REF] Palczewski | Evidence for compartmentalization of mammalian carotenoid metabolism[END_REF][START_REF] Raghuvanshi | Cellular localization of beta-carotene 15,15' oxygenase-1 (BCO1) and beta-carotene 9',10' oxygenase-2 (BCO2) in rat liver and intestine[END_REF]. BCO2 seems to play an important role in lycopene metabolism since Bco2 -/-mice fed with tomato/lycopene enriched diets accumulate more lycopene in their liver than their wild type or Bco1 - /-littermates [START_REF] Ford | Loss of carotene-9',10'-monooxygenase expression increases serum and tissue lycopene concentrations in lycopene-fed mice[END_REF][START_REF] Ford | Genetic ablation of carotene oxygenases and consumption of lycopene or tomato powder diets modulate carotenoid and lipid metabolism in mice[END_REF][START_REF] Tan | beta-Carotene-9',10'-oxygenase status modulates the impact of dietary tomato and lycopene on hepatic nuclear receptor-, stress-, and metabolism-related gene expression in mice[END_REF], suggesting a higher absorption and/or a decreased degradation of lycopene. BCO2 affinity seems to vary across species: recombinant ferret BCO2 displayed activity only towards 5-cis and 13-cis lycopene isoforms, but not towards the all-trans isoform [START_REF] Hu | The biochemical characterization of ferret carotene-9',10'-monooxygenase catalyzing cleavage of carotenoids in vitro and in vivo[END_REF]). Previous work from Kiefer et al. indicated the production of apolycopenal in a lycopene-producing E. coli strain expressing mouse BCO2 [START_REF] Kiefer | Identification and characterization of a mammalian enzyme catalyzing the asymmetric oxidative cleavage of provitamin A[END_REF])By analogy with retinal (that can be further transformed to either retinol or retinoic acid), it has been suggested that apolycopenals could be metabolized to the corresponding acid or alcohol forms (i.e. apolycopenoic acid or apolycopenol). Hu et al. have been able to produce apo-10'lycopenol and apo-10'-lycopenoic acid by incubating apo-10'-lycopenal with ferret liver homogenates, suggesting that the enzymatic equipment allowing such metabolisation is present in mammals [START_REF] Hu | The biochemical characterization of ferret carotene-9',10'-monooxygenase catalyzing cleavage of carotenoids in vitro and in vivo[END_REF]). However, the in vivo relevance of apocarotenoic acids/apocarotenols remains questionable since so far, they have only been observed in Bco1 -/-mice fed with xanthophyll or beta-carotene rich diets [START_REF] Amengual | A mitochondrial enzyme degrades carotenoids and protects against oxidative stress[END_REF].

In humans, adipose tissue and liver are the main lycopene storage tissues, and contain about 60% and 30%, respectively, of the total lycopene body stores [START_REF] Chung | Site-specific concentrations of carotenoids in adipose tissue: relations with dietary and serum carotenoid concentrations in healthy adults[END_REF][START_REF] Landrier | Lipophilic micronutrients and adipose tissue biology[END_REF][START_REF] Moran | Complex interactions between dietary and genetic factors impact lycopene metabolism and distribution[END_REF]. Furthermore, it has been observed that lycopene bioavailability and deposition in the liver of rodents is increased in the context of a high fat diet [START_REF] Bernal | Lipid biomarkers and metabolic effects of lycopene from tomato juice on liver of rats with induced hepatic steatosis[END_REF][START_REF] Martin-Pozuelo | The effect of tomato juice supplementation on biomarkers and gene expression related to lipid metabolism in rats with induced hepatic steatosis[END_REF]. In the liver, the uptake of lycopene is probably mediated via chylomicrons-remnant receptors, i.e. the LDL-receptor (LDLR), the LDL-receptor related protein 1 (LRP1), and the heparan sulfate proteoglycans (HSPGs) [START_REF] Dallinga-Thie | The metabolism of triglyceride-rich lipoproteins revisited: new players, new insight[END_REF]. In adipose tissue and adipocytes, the uptake of lycopene is mediated, at least in part, by CD36 [START_REF] Moussa | CD36 is involved in lycopene and lutein uptake by adipocytes and adipose tissue cultures[END_REF] and is not related to its physicochemical properties [START_REF] Sy | Effects of physicochemical properties of carotenoids on their bioaccessibility, intestinal cell uptake, and blood and tissue concentrations[END_REF]. Lycopene is then stored in the adipocyte lipid droplets and in membranes [START_REF] Gouranton | Purified low-density lipoprotein and bovine serum albumin efficiency to internalise lycopene into adipocytes[END_REF]. It is generally assumed that lycopene in these tissues could mediate some biological effects. It is worth noting that these tissues are also key sites in the development of inflammatory processes in the context of the metabolic syndrome and more generally for cardiometabolic disorders.

In terms of biological function, lycopene is well known for its antioxidant properties [START_REF] Palozza | Tomato lycopene and inflammatory cascade: basic interactions and clinical implications[END_REF]. It displays anti free radical properties mediated by eleven conjugated double bonds. It is essential in scavenging lipid peroxyl radicals, reactive oxygen species, and nitric oxide [START_REF] Engelmann | Nutritional aspects of phytoene and phytofluene, carotenoid precursors to lycopene[END_REF]. In addition, several studies have shown that lycopene displays antiinflammatory effects in several tissues and in several pathophysiological disorders, which are highly relevant in the context of cardiometabolic disorders. This is notably the case in adipocytes and adipose tissue [START_REF] Gouranton | Lycopene inhibits proinflammatory cytokine and chemokine expression in adipose tissue[END_REF], Marcotorchino et al. 2012[START_REF] Luvizotto Rde | Lycopene supplementation modulates plasma concentrations and epididymal adipose tissue mRNA of leptin, resistin and IL-6 in diet-induced obese rats[END_REF][START_REF] Singh | A novel cobiotic-based preventive approach against high-fat diet-induced adiposity, nonalcoholic fatty liver and gut derangement in mice[END_REF][START_REF] Fenni | Lycopene and tomato powder supplementation similarly inhibit high-fat diet induced obesity, inflammatory response, and associated metabolic disorders[END_REF], in the liver [START_REF] Wang | Lycopene metabolism and its biological significance[END_REF][START_REF] Ip | Lycopene metabolite, apo-10'-lycopenoic acid, inhibits diethylnitrosamine-initiated, high fat diet-promoted hepatic inflammation and tumorigenesis in mice[END_REF], and in arterial wall cells [START_REF] Armoza | Tomato extract and the carotenoids lycopene and lutein improve endothelial function and attenuate inflammatory NF-kappaB signaling in endothelial cells[END_REF][START_REF] Hung | Lycopene inhibits TNF-alpha-induced endothelial ICAM-1 expression and monocyte-endothelial adhesion[END_REF]) .

Among others, these anti-inflammatory effects could be responsible for the numerous health effects attributed to lycopene [START_REF] Wang | Lycopene metabolism and its biological significance[END_REF][START_REF] Story | An update on the health effects of tomato lycopene[END_REF] in the field of liver steatosis [START_REF] Wang | Lycopene metabolism and its biological significance[END_REF], cardiovascular diseases [START_REF] Muller | Lycopene and Its Antioxidant Role in the Prevention of Cardiovascular Diseases-A Critical Review[END_REF][START_REF] Thies | Effect of a tomato-rich diet on markers of cardiovascular disease risk in moderately overweight, disease-free, middle-aged adults: a randomized controlled trial[END_REF], adiposity and obesity (Bonet et al. 2015, Landrier, Marcotorchino, and[START_REF] Landrier | Lipophilic micronutrients and adipose tissue biology[END_REF]. Indeed, inflammation appears as a major hallmark and is intimately related to all cardiometabolic disorders, including type-2 diabetes, cardiovascular diseases and metabolic syndrome [START_REF] Esser | Inflammatory markers and cardiometabolic diseases[END_REF]. It is noteworthy that obesity is a major driving force of inflammation and thus constitutes a major risk factor for the development of these disorders. Obesity is characterized by a chronic low-grade or metabolic inflammation, which mainly originates from adipose tissue enlargement [START_REF] Gregor | Inflammatory mechanisms in obesity[END_REF]. Pro-inflammatory cytokines, such as tumor necrosis factor alpha (TNF-α), interleukin (IL)-6, and IL-1β, and chemokines, such as MCP1/CCL2 or RANTES/CCL5, are produced by adipose tissue [START_REF] Tourniaire | Chemokine Expression in Inflamed Adipose Tissue Is Mainly Mediated by NF-kappaB[END_REF]) and are widely acknowledged as important pathophysiological factors implicated in insulin resistance. In addition, these markers of inflammation also stimulate the production of acute phase proteins, such as C-reactive protein (CRP), serum amyloid A, and adhesion molecules, such as soluble intercellular adhesion molecule type 1 (sICAM1) or VCAM, in the liver, which are markers of increased CVD risk (Pearson et al. 2003, Ridker and[START_REF] Ridker | C-reactive protein, inflammation, and coronary risk[END_REF].

The overall objective of the present review is to summarize the knowledge related to the antiinflammatory effects of lycopene, its metabolites, tomatoes, tomato products or extracts, from cell culture to human clinical studies that could beneficially impact prevalence of cardiometabolic disorders.

In vitro studies

We have demonstrated the ability of lycopene to inhibit the expression of pro-inflammatory cytokines and chemokines in vitro in murine and human adipocytes [START_REF] Gouranton | Lycopene inhibits proinflammatory cytokine and chemokine expression in adipose tissue[END_REF]). These data were also reproduced ex vivo in adipose tissue explants from mice subjected to a high fat diet (characterized by low-grade inflammation), and confirmed by another group in human adipocytes [START_REF] Warnke | Combinations of bio-active dietary constituents affect human white adipocyte function in-vitro[END_REF]). The molecular mechanism was investigated and the involvement of NF-κB was confirmed [START_REF] Gouranton | Lycopene inhibits proinflammatory cytokine and chemokine expression in adipose tissue[END_REF]. Similar results (i.e., inhibition of cytokine and chemokine expression in various in vitro and ex vivo models) were obtained with apo-10′-lycopenoic acid, a metabolite of lycopene [START_REF] Gouranton | Apo-10'-lycopenoic acid impacts adipose tissue biology via the retinoic acid receptors[END_REF]. Finally, lycopene attenuated LPS-mediated induction of TNFα in macrophages via NF-κB and JNK, as well as macrophage migration in vitro [START_REF] Marcotorchino | Lycopene attenuates LPS-induced TNF-alpha secretion in macrophages and inflammatory markers in adipocytes exposed to macrophageconditioned media[END_REF]. Consequently, lycopene decreased macrophage-induced cytokine, acute phase protein and chemokine mRNA levels in adipocytes. Similarly, tomato extract suppressed inflammation and pro-inflammatory cytokine production during interaction between adipocytes and macrophages [START_REF] Kim | Tomato extract suppresses the production of proinflammatory mediators induced by interaction between adipocytes and macrophages[END_REF].

In macrophages, lycopene [START_REF] Zou | Lycopene suppresses proinflammatory response in lipopolysaccharide-stimulated macrophages by inhibiting ROS-induced trafficking of TLR4 to lipid raft-like domains[END_REF][START_REF] Hadad | The synergistic anti-inflammatory effects of lycopene, lutein, beta-carotene, and carnosic acid combinations via redox-based inhibition of NF-kappaB signaling[END_REF][START_REF] Feng | Lycopene suppresses LPS-induced NO and IL-6 production by inhibiting the activation of ERK, p38MAPK, and NF-kappaB in macrophages[END_REF] and tomato aqueous extracts (Schwager et al. 2016, Navarrete, Alarcon, and[START_REF] Navarrete | Aqueous Extract of Tomato (Solanum lycopersicum L.) and Ferulic Acid Reduce the Expression of TNF-alpha and IL-1beta in LPS-Activated Macrophages[END_REF] suppressed the pro-inflammatory response (including cytokines and interleukins such as TNFα, IL-1β, IL-6…) in RAW264.7 cells incubated with LPS, probably via an inhibition of the NF-κB pathway [START_REF] Hadad | The synergistic anti-inflammatory effects of lycopene, lutein, beta-carotene, and carnosic acid combinations via redox-based inhibition of NF-kappaB signaling[END_REF]. In THP-1 cells, lycopene also prevented the activation of the pro-inflammatory cascade mediated by oxysterols via an inhibition of NF-κB and an increase in PPARγ expression. Lycopene synthetic metabolites apo-10'-lycopenoic acid and apo-14'-lycopenoic acid repressed NF-κB activation induced by cigarette smoke extract in THP-1 macrophage cells [START_REF] Catalano | Comparative antioxidant effects of lycopene, apo-10'-lycopenoic acid and apo-14'lycopenoic acid in human macrophages exposed to H2O2 and cigarette smoke extract[END_REF]) and MMP9 expression [START_REF] Palozza | Modulation of MMP-9 pathway by lycopene in macrophages and fibroblasts exposed to cigarette smoke[END_REF].

The effects of lycopene and tomato products have been studied in several vascular endothelial cell models, including HUVEC cells. In these cells, tomato oleoresin and purified lycopene significantly inhibited the TNFα-mediated expression of several adhesion molecules, including ICAM-1 and VCAM-1, via a decrease in NF-κB signaling (decrease in IκB protein concentration and p65 phosphorylation level) [START_REF] Armoza | Tomato extract and the carotenoids lycopene and lutein improve endothelial function and attenuate inflammatory NF-kappaB signaling in endothelial cells[END_REF][START_REF] Hung | Lycopene inhibits TNF-alpha-induced endothelial ICAM-1 expression and monocyte-endothelial adhesion[END_REF]. Tomato extracts also limited the gene expression of proinflammatory cytokines and chemokines (TNFα and IL-8) and induced the gene expression of antiinflammatory cytokines, such as IL-10, after stimulation by TNFα, possibly by targeting NF-κB signaling [START_REF] Hazewindus | Protection against chemotaxis in the anti-inflammatory effect of bioactives from tomato ketchup[END_REF][START_REF] Di Tomo | beta-Carotene and lycopene affect endothelial response to TNF-alpha reducing nitro-oxidative stress and interaction with monocytes[END_REF]. This effect was associated with a limitation of monocyte migration. Similarly, tomato aqueous extracts blunted the production of a large range of interleukins and chemokines [START_REF] Schwager | Tomato Aqueous Extract Modulates the Inflammatory Profile of Immune Cells and Endothelial Cells[END_REF]. Interestingly, lycopene has also been depicted as able to attenuate the activation of cell signaling related to inflammation, such as ERK, p38 [START_REF] Chen | Lycopene inhibits angiogenesis both in vitro and in vivo by inhibiting MMP-2/uPA system through VEGFR2mediated PI3K-Akt and ERK/p38 signaling pathways[END_REF], PI3K/AKT or NRF2 [START_REF] Sung | Lycopene inhibits cyclic strain-induced endothelin-1 expression through the suppression of reactive oxygen species generation and induction of heme oxygenase-1 in human umbilical vein endothelial cells[END_REF]. Other pro-inflammatory stimuli have been tested regarding the ability of lycopene to blunt the pro-inflammatory responses to HMGB1 (high mobility group box 1; [START_REF] Lee | Inhibitory effects of lycopene on HMGB1-mediated pro-inflammatory responses in both cellular and animal models[END_REF])) or LPS [START_REF] Wang | Lycopene protects against LPSinduced proinflammatory cytokine cascade in HUVECs[END_REF].

Preclinical studies

Cardiovascular diseases

In the context of hyperhomocysteinemia, a risk factor for atherosclerosis, lycopene supplementation (10-20 mg/kg of body weight p.o. for 10 weeks) has been shown to reduce aortic lipid depots and decrease the level of numerous circulating inflammatory markers (serum VCAM1, MCP1, IL-8) [START_REF] Liu | Effect of lycopene on the vascular endothelial function and expression of inflammatory agents in hyperhomocysteinemic rats[END_REF].

In vivo myocardial ischemia/reperfusion (I/R) injury models are used to study the potential of compounds to limit damages associated with this event. Whereas several studies have been published regarding the benefits of lycopene treatment on I/R induced cardiac dysfunctions [START_REF] Xu | Lycopene Protects against Hypoxia/Reoxygenation Injury by Alleviating ER Stress Induced Apoptosis in Neonatal Mouse Cardiomyocytes[END_REF], only a few have documented the consequences on inflammatory aspects. It was shown in rats by Bansal et al. that oral administration of lycopene for 31 days (1 mg/kg), before inducing a chirurgical occlusion of the left anterior ascending coronary artery followed by a reperfusion, was able to diminish damages associated with I/R, as well as inflammatory cell infiltration in myocardial tissue [START_REF] Bansal | Cardioprotective effect of lycopene in the experimental model of myocardial ischemia-reperfusion injury[END_REF]). He et al. have demonstrated that chronic lycopene intake (10 mg/kg body weight daily for 4 weeks) can protect against inflammation associated with post-myocardial infarction remodeling, as shown by diminished expression of Tnf and Il1b mRNAs and decreased NF-κB activation in mice [START_REF] He | Lycopene attenuates inflammation and apoptosis in post-myocardial infarction remodeling by inhibiting the nuclear factor-kappaB signaling pathway[END_REF]. Using an opposite approach, i.e. inducing cardiac remodeling first then administering lycopene (40 mg/kg body weight daily for 28 days), Wang et al. found that lycopene could partially maintain cardiac function and limit p38 activation pathway [START_REF] Wang | Protective effect of lycopene on cardiac function and myocardial fibrosis after acute myocardial infarction in rats via the modulation of p38 and MMP-9[END_REF]. This was in agreement with what was previously reported by another group using another model of myocardial infarction (induced by isoproterenol): lycopene intake also diminished the infarction area size and inflammation assessed by measurement of circulating CRP level and tissular myeloperoxidase activity [START_REF] Upaganlawar | Effect of vitamin E alone and in combination with lycopene on biochemical and histopathological alterations in isoproterenol-induced myocardial infarction in rats[END_REF]. In a recent study, Tong et al. showed that acute (intravenous) lycopene administration just after ischemia diminished the size of the infarct and that it was associated with a decrease in the activation of two inflammatory signaling pathways, JNK and p42/44 MAPK [START_REF] Tong | Intravenous Administration of Lycopene, a Tomato Extract, Protects against Myocardial Ischemia-Reperfusion Injury[END_REF].

Obesity models / metabolic syndrome / non-alcoholic liver steatosis

Several groups have explored the anti-inflammatory effects of tomato extract and/or lycopene intake -mainly in rats -in the context of obesity. In these studies, obesity was induced using high fat diets, associated or not with administration of lycopene, and the consequences of lycopene intake on obesity onset was observed. Bahcecioglu and colleagues have used two doses of lycopene (2 and 4 mg/kg bw/d for 6 weeks) and reported beneficial effects of lycopene administration on insulin sensitivity, liver steatosis and inflammation in rats only at a dose of 2 mg/kg/d [START_REF] Bahcecioglu | Lycopene prevents development of steatohepatitis in experimental nonalcoholic steatohepatitis model induced by high-fat diet[END_REF]). However, the highest dose led to fewer beneficial changes, suggesting that there might be an optimal dosage of lycopene in this context. A similar observation was made in a study involving mice fed with a high fat and high cholesterol (HF/HChol) diet supplemented with two doses of dried tomato peel (9% or 17%) for 12 weeks [START_REF] Zidani | Effect of dry tomato peel supplementation on glucose tolerance, insulin resistance, and hepatic markers in mice fed high-saturated-fat/high-cholesterol diets[END_REF].

Overall, all studies have highlighted the decrease in local and/or systemic inflammation following tomato product/lycopene consumption, except in the studies by the group of Periago [START_REF] Bernal | Lipid biomarkers and metabolic effects of lycopene from tomato juice on liver of rats with induced hepatic steatosis[END_REF][START_REF] Martin-Pozuelo | The effect of tomato juice supplementation on biomarkers and gene expression related to lipid metabolism in rats with induced hepatic steatosis[END_REF]. However, it should be noted that in these papers, the HF/HChol diet had no effect on circulating TNFα or IL-6 compared with control diet, neither did it induce signs of inflammation in the liver, as assessed histologically. Conversely, a similar study performed with lycopene (isolated from algae or tomato) supplementation of HF/HChol diet led to a reduced degree of liver steatosis and atherosclerotic depot in the aorta, as well as anti-inflammatory effects (decreased serum levels of ceruloplasmine, CRP, myeloperoxidase and decreased 15-lipoxygenase and cyclo-oxygenase activities in PBMCs) [START_REF] Renju | Effect of lycopene from Chlorella marina on high cholesterol-induced oxidative damage and inflammation in rats[END_REF]. Interestingly, the lycopene from algae origin displayed more beneficial effects than the one isolated from tomato, suggesting that synergetic effects with other compounds present in these matrices play a role in this effect.

Others have used a model of diet-induced obesity (DIO) involving high fat diet plus sucrose, supplemented or not with lycopene, in rats. Under these conditions, it was observed that lycopene treatment improved insulin resistance related parameters (plasma insulin, glycemia) and could limit inflammation at the central level (decreased plasma concentrations of leptin, resistin and IL6 but not TNFα), in the adipose tissue (decreased mRNA expression of leptin, resistin, Mcp1 and Il6 but not Tnf) [START_REF] Luvizotto Rde | Lycopene supplementation modulates plasma concentrations and epididymal adipose tissue mRNA of leptin, resistin and IL-6 in diet-induced obese rats[END_REF]) and also in the brain [START_REF] Yin | Lycopene attenuates insulin signaling deficits, oxidative stress, neuroinflammation, and cognitive impairment in fructose-drinking insulin resistant rats[END_REF]). Pierine et al. investigated the effect of lycopene supplementation in rats already obese, and again the absence of effect on the circulating level of TNFα was observed, whereas a decreased production was found in the kidney [START_REF] Pierine | Lycopene supplementation reduces TNF-alpha via RAGE in the kidney of obese rats[END_REF].

In a very extensive study, the anti-inflammatory effects of lycopene at a dose of 10 mg/kg of diet limited adiposity, prevented hyperinsulinemia and insulin-resistance during the development of DIO in mice. It was found that lycopene limited liver steatosis and inflammation (by decreasing IL6, TNFα, NF-κB and TLR4) [START_REF] Singh | A novel cobiotic-based preventive approach against high-fat diet-induced adiposity, nonalcoholic fatty liver and gut derangement in mice[END_REF]). Furthermore, it was found that some circulating inflammatory markers (TNFα, IL-1β) were also diminished by lycopene administration, suggesting a generic antiinflammatory effect. This study also indicated for the first time that lycopene could act on the gut microbiota, as reflected by caecal bacterial abundance and short chain fatty acid production. The limitation of bacteria and the preservation of gut and colon epithelial integrity associated with obesity and insulin-resistance, could participate in the overall anti-inflammatory properties of lycopene at the systemic level.

In agreement, we also recently showed that supplementation with lycopene or tomato powder (TP) for 12 weeks diminished the hepatic steatosis induced by high fat diet [START_REF] Fenni | Lycopene and tomato powder supplementation similarly inhibit high-fat diet induced obesity, inflammatory response, and associated metabolic disorders[END_REF], and reported an anti-inflammatory effect of lycopene or TP in the liver but also an impact on lipid metabolism. We also investigated the impact of lycopene and TP supplementation on adipose tissue inflammatory status, and reported an overall down-regulation of genes encoding pro-inflammatory markers such as cytokines (Il6, Tnfα), adipokines (resistin, visfatin, leptin), acute phase proteins (Saa3, haptoglobin) and chemokines (Ccl5, Mcp1, Cxcl10), a down-regulation of genes encoding metalloproteinases (Mmp3 and Mmp9), and an up-regulation of genes encoding anti-inflammatory proteins such as Il-10 and Tgf-β. In addition, we confirmed at the protein level that some of these pro-inflammatory markers (IL-6, TNFα, MCP1, CCL5) were reduced compared to HFD conditions. We also observed that lycopene and TP strongly reduced the phosphorylation levels of p65 and IκB in vivo, consistently with in vitro data, which suggests that the anti-inflammatory effect of lycopene and TP on adipose tissue results from their ability to inhibit NF-κB signaling in adipose tissue. Despite this clear impact of lycopene or TP on the NF-κB signaling pathway, we cannot exclude that this effect was strictly due to the reduced adiposity, which could result in a deactivation of this signaling pathway.

Since apo-10'-lycopenoids are proposed to be the major products of lycopene metabolism via BCO2, the effect of apo-10'-lycopenoic acid towards liver steatosis was investigated in the genetically obese mouse model Ob/Ob [START_REF] Chung | Apo-10'-lycopenoic acid, a lycopene metabolite, increases sirtuin 1 mRNA and protein levels and decreases hepatic fat accumulation in ob/ob mice[END_REF]. It was found that apo-10'-lycopenoic acid supplementation diminished the severity of liver steatosis and that this result was mediated by SIRT1 which, among other functions, is able to inhibit NF-κB inflammatory signaling [START_REF] Schug | Sirtuin 1 in lipid metabolism and obesity[END_REF]. In a study comparing the potential of lycopene vs. apo-10'-lycopenoic acid to limit liver steatosis in DIO Bco2 -/- mice, Ip et al. identified that these two molecules had distinct modes of action, with apo-10' lycopenoic acid acting on SIRT1 activity and expression in the liver while lycopene was acting at the level of mesenteric fat through actions of PPARα and PPARγ [START_REF] Ip | Lycopene and apo-10'-lycopenoic acid have differential mechanisms of protection against hepatic steatosis in beta-carotene-9',10'-oxygenase knockout male mice[END_REF]. Another nuclear receptor that could mediate anti-inflammatory effects of lycopene and apo-10'-lycopenoic acid is RAR (retinoic acid receptor). We showed that apo-10'-lycopenoic acid was able to transactivate RAR, concomitantly to the prevention of the production of inflammatory markers in adipocytes [START_REF] Gouranton | Apo-10'-lycopenoic acid impacts adipose tissue biology via the retinoic acid receptors[END_REF]. Furthermore, Aydemir et al. have shown that lycopene administration to mice could lead to the induction of RARE-mediated cell signaling and could even restore vitamin A deficiency [START_REF] Aydemir | Lycopene induces retinoic acid receptor transcriptional activation in mice[END_REF].

Human studies

Cross-sectional and prospective studies

Adjusted circulating concentrations of lycopene as well as many other carotenoids were inversely associated with CRP concentrations in large studies, such as the National Health and Nutrition Examination Survey III (NHANES III) [START_REF] Ford | C-reactive protein concentration and concentrations of blood vitamins, carotenoids, and selenium among United States adults[END_REF][START_REF] Kritchevsky | Serum carotenoids and markers of inflammation in nonsmokers[END_REF], and in other studies [START_REF] Kim | Independent inverse relationship between serum lycopene concentration and arterial stiffness[END_REF]. High plasma lycopene concentration (reflected by the highest tertile) was associated with lower plasma CRP, IL-6 and MMP9 concentrations in a general population of 285 Swedish men and women before adjustment but these associations were lost after adjustments for age, sex, alcohol intake, BMI, systolic blood pressure and total cholesterol [START_REF] Ryden | Provitamin A carotenoids are independently associated with matrix metalloproteinase-9 in plasma samples from a general population[END_REF].

Similarly, lycopene status was inversely associated with IL-6 [START_REF] Walston | Serum antioxidants, inflammation, and total mortality in older women[END_REF]) and other markers of endothelial function, such as s-ICAM 1 (van [START_REF] Van Herpen-Broekmans | Serum carotenoids and vitamins in relation to markers of endothelial function and inflammation[END_REF]). In the YALTA prospective study, the initial plasma level of lycopene was inversely associated with sICAM1 protein level 15 years later [START_REF] Hozawa | Relationships of circulating carotenoid concentrations with several markers of inflammation, oxidative stress, and endothelial dysfunction: the Coronary Artery Risk Development in Young Adults (CARDIA)/Young Adult Longitudinal Trends in Antioxidants (YALTA) study[END_REF]. Similarly, plasma lycopene concentration was inversely associated with plasma CRP and E-selectin concentrations in men and women of an Aboriginal population and the association remained significant after adjustment for classical confounding factors [START_REF] Rowley | Inflammation and vascular endothelial activation in an Aboriginal population: relationships to coronary disease risk factors and nutritional markers[END_REF]. No relationship was found between plasma lycopene and plasma CRP concentrations in a population of middle-aged and older women [START_REF] Wang | Associations of plasma carotenoids with risk factors and biomarkers related to cardiovascular disease in middle-aged and older women[END_REF].

Interventions studies Lycopene supplementation

Short-term lycopene supplementation (1 week; 80 mg/day) in young healthy participants did not modify post-prandial inflammatory status (CRP, ICAM or VCAM) [START_REF] Denniss | Effect of short-term lycopene supplementation and postprandial dyslipidemia on plasma antioxidants and biomarkers of endothelial health in young, healthy individuals[END_REF]). In obese people, 4 weeks of lycopene supplementation (Lyc-O-Mato, 30 mg/day) did not modify plasma concentrations of inflammatory markers (TNFα, IL6, CRP) [START_REF] Markovits | The effect of tomato-derived lycopene on low carotenoids and enhanced systemic inflammation and oxidation in severe obesity[END_REF].

Similarly, no impact of 1-week lycopene supplementation (7 mg/day) was observed in patients with prehypertension [START_REF] Petyaev | Whey protein lycosome formulation improves vascular functions and plasma lipids with reduction of markers of inflammation and oxidative stress in prehypertension[END_REF] and no impact of 2 weeks supplementation (lyc-O-Mato 3 capsules /days; approx. 14.64 mg of lycopene/day) was observed on TNFα in a smoker (n=12) and non-smoker (n=15) population [START_REF] Briviba | Effects of supplementing a low-carotenoid diet with a tomato extract for 2 weeks on endogenous levels of DNA single strand breaks and immune functions in healthy nonsmokers and smokers[END_REF]). Seven mg per day of lycopene for 2 months also did not modify inflammatory markers in CVD patients and healthy volunteers (n=36 for each group) [START_REF] Gajendragadkar | Effects of oral lycopene supplementation on vascular function in patients with cardiovascular disease and healthy volunteers: a randomised controlled trial[END_REF]. In a large study involving 224 healthy middle-aged volunteers supplemented for 12 weeks with lycopene capsules (10 mg/day) or tomato-rich diet, no modification of inflammatory markers was observed [START_REF] Thies | Effect of a tomato-rich diet on markers of cardiovascular disease risk in moderately overweight, disease-free, middle-aged adults: a randomized controlled trial[END_REF]).

On the opposite, it was reported that 12 week of a lycopene-rich diet (224-350 mg lycopene/week) in overweight middle-aged individuals (n=54) led to a reduction in SAA concentration in the HDL 3 fraction while 12 weeks of lycopene supplementation (70 mg/week) in the same study group led to a reduction in SAA concentration in both plasma and HDL 3 fraction [START_REF] Mceneny | Lycopene intervention reduces inflammation and improves HDL functionality in moderately overweight middle-aged individuals[END_REF]). Lycopene supplementation (7 mg or 15 mg /day for 8 weeks) also reduced CRP, ICAM and VCAM plasma concentrations in healthy men (n=126) [START_REF] Kim | Effects of lycopene supplementation on oxidative stress and markers of endothelial function in healthy men[END_REF]. In 26 healthy volunteers, Lyco-O-Mato (5.7 mg of lycopene) for 26 days reduced TNFα production from whole blood after a LPS challenge in vitro [START_REF] Riso | Effect of a tomato-based drink on markers of inflammation, immunomodulation, and oxidative stress[END_REF].

Tomato supplementation

In type-2 diabetes patients (n=15), tomato juice supplementation for 4 weeks (300 ml/day) did not improve CRP, ICAM or VCAM plasma concentrations compared to placebo [START_REF] Upritchard | Effect of supplementation with tomato juice, vitamin E, and vitamin C on LDL oxidation and products of inflammatory activity in type 2 diabetes[END_REF]. It has been established that a tomato-rich diet (300 g/day for 1 month) had no impact on plasma concentrations of E-selectin, ICAM and CRP in 103 healthy volunteers [START_REF] Blum | Tomato-rich (Mediterranean) diet does not modify inflammatory markers[END_REF]. Tomato juice consumption for 2 weeks in healthy volunteers (daily dose of lycopene: 20.6 mg) reduced plasma CRP concentration [START_REF] Jacob | Influence of lycopene and vitamin C from tomato juice on biomarkers of oxidative stress and inflammation[END_REF]. Interestingly, in heathy overweight volunteers, a combination of dietary anti-inflammatory molecules, including tomato extract, resulted in an increase in plasma adiponectin concentration, that displays anti-inflammatory activity , whereas no other modification was observed [START_REF] Bakker | An antiinflammatory dietary mix modulates inflammation and oxidative and metabolic stress in overweight men: a nutrigenomics approach[END_REF]. This result suggests that this antiinflammatory mix could have a beneficial impact on adipose tissue inflammatory status as reflected by adiponectin [START_REF] Ruhl | Dietary regulation of adiponectin by direct and indirect lipid activators of nuclear hormone receptors[END_REF]. Consumption of tomato product blunted the post-prandial induction of plasma IL-6 in healthy women and men (n=25) [START_REF] Burton-Freeman | Protective activity of processed tomato products on postprandial oxidation and inflammation: a clinical trial in healthy weight men and women[END_REF]. Plasma concentration of lycopene, related to the consumption of a diet rich in fruits and vegetables, was inversely associated with a decrease in the LPS-mediated production of IL-6 in PMBC of overweight women [START_REF] Yeon | Diets rich in fruits and vegetables suppress blood biomarkers of metabolic stress in overweight women[END_REF]). An intake of 330 ml/day of tomato juice for 20 days also significantly reduced plasma concentrations of IL-8 and TNFα in overweight participants while only IL-6 was diminished in obese participants [START_REF] Ghavipour | Tomato juice consumption reduces systemic inflammation in overweight and obese females[END_REF]. A similar reduction in plasma concentrations of plasma IL-6 and TNFα was observed in patients with metabolic syndrome who consumed tomato juice 4 times a week for a period of 2 months [START_REF] Tsitsimpikou | Dietary supplementation with tomato-juice in patients with metabolic syndrome: a suggestion to alleviate detrimental clinical factors[END_REF]. A reduction in CRP concentrations was also observed in women with heart failure but not in men following consumption of a vegetable juice (340 ml/d?) for 30 days [START_REF] Biddle | Lycopene dietary intervention: a pilot study in patients with heart failure[END_REF]. In women (n=30), tomato juice consumption (280 ml/day containing 32.5 mg of lycopene) for 8 weeks reduced MCP1 and increased adiponectin [START_REF] Li | Tomato juice supplementation in young women reduces inflammatory adipokine levels independently of body fat reduction[END_REF], but no control group was used in this study. A similar reduction in MCP1 was observed in healthy people (n=12) consuming a vegetable soup constituted mainly of tomato (500 ml/day for 14 days) [START_REF] Sanchez-Moreno | Mediterranean vegetable soup consumption increases plasma vitamin C and decreases F2-isoprostanes, prostaglandin E2 and monocyte chemotactic protein-1 in healthy humans[END_REF]. The postprandial impact of a tomato-rich diet (3 types: raw tomatoes, tomato sauce or tomato sauce with olive oil) was evaluated in healthy volunteers (n=40): such regimens led to a reduction in MCP1 and to an increase in IL-10 for the 3 diets, and an increase in IL18 for tomato sauce. IL6 and VCAM were reduced with tomato sauce plus olive oil [START_REF] Valderas-Martinez | Tomato Sauce Enriched with Olive Oil Exerts Greater Effects on Cardiovascular Disease Risk Factors than Raw Tomato and Tomato Sauce: A Randomized Trial[END_REF]. A similar postprandial impact of carotenoid-rich tomato extract has been depicted in 146 healthy normal weight individuals regarding the level of oxidized LDL [START_REF] Deplanque | Proprietary tomato extract improves metabolic response to high-fat meal in healthy normal weight subjects[END_REF]. A recent meta-analysis reported a significant decrease in IL-6 (standardized mean difference -0.25; p = 0.03) following tomato supplementation [START_REF] Cheng | Tomato and lycopene supplementation and cardiovascular risk factors: A systematic review and meta-analysis[END_REF].

Negative results associated with tomato juice supplementation (330 ml/day for 2 weeks in 22 healthy men) were reported in only one study where an increase in TNFα production was observed compared to wash-out periods [START_REF] Watzl | Supplementation of a lowcarotenoid diet with tomato or carrot juice modulates immune functions in healthy men[END_REF]. The origin of this contrasted results is presently unknown.

Conclusion and perspectives

Based on the reported studies, the anti-inflammatory effect of lycopene and /or tomato products is rather clear, at least in vitro and preclinical studies. Indeed, in several cell models related to cardiometabolic health, lycopene or tomato products exert a strong and reproducible inhibition on the expression and secretion of cytokines and interleukins, as well as an impact on several signaling pathways that could participate to the overall effect including NF-κB. Preclinical studies also strongly support these data. In most cases, an improvement of the inflammatory status has been reported in vivo, in key tissues (mainly in the liver and in adipose tissue) and in some cases at the systemic level.

All data generated in the context of obesity and its comorbidities suggest that lycopene, and in some cases tomato products, have a beneficial impact on inflammation in key organs and are also associated with clear improvement in metabolic disorders.

Interestingly, this anti-inflammatory effect of lycopene and/or tomato product has been confirmed in some clinical studies at the systemic level. If the effect of isolated lycopene on inflammatory status is still not clear, it is noteworthy that most studies have been realized in healthy people with no inflammation disorders. When tomato supplements are administered to volunteers with an elevated inflammatory status (overweight/obesity or during post-prandial inflammation), it becomes clearer that such a nutritional approach may have beneficial effects on the control of inflammation. In lane with this, it is important to specify that lycopene is classically associated with the health benefits of tomato consumption [START_REF] Canene-Adams | The tomato as a functional food[END_REF]). However, these data show that the overlap between lycopene supplementation and tomato product consumption is not perfect regarding their health effects (Burton-Freeman and Sesso 2014).

Several important questions remain unsolved yet. It is presently not clear if the all-trans or cis isomers of lycopene display similar activities regarding their anti-inflammatory effect. It is also not clear if lycopene or its metabolites are responsible for the reported effects. Indeed, the activity of lycopene metabolites towards inflammation has been barely investigated so far.. It is also noteworthy that in the case of tomato product consumption, it cannot be excluded that the reported effects are only due to lycopene and not to others molecules present, or even to the association of lycopene with other molecules. All these questions require further investigations in vitro and in preclinical models.

Regarding clinical studies, it is important to specify that the anti-inflammatory effects of lycopene and /or tomato products have been evaluated only on plasma. Thus, it will be of particular interest to confirm some of these data in tissue biopsies (notably from adipose tissue) to evaluate the contribution of lycopene or tomato products on tissue inflammation at a local level.

Finally, if the NF-κB signaling pathway is highly suspected to be deactivated by lycopene or its metabolites, the precise molecular mechanism leading to this anti-inflammatory effect is presently not elucidated. Several mechanisms could be involved, such as an effect of the expression of phosphatases involved in the dephosphorylation of NF-κB proteins, physical interaction with NF-kB signaling proteins or effect on the production of lipid mediators such as resolvins. The fact that lycopene or its metabolites transactivated RAR [START_REF] Aydemir | Lycopene induces retinoic acid receptor transcriptional activation in mice[END_REF][START_REF] Gouranton | Apo-10'-lycopenoic acid impacts adipose tissue biology via the retinoic acid receptors[END_REF]) also suggests that this nuclear receptor could be involved in lycopene-dependent antiinflammatory effect since we recently reported that all-trans retinoic acid blunted the inflammation process in adipocytes [START_REF] Karkeni | Obesity-associated Inflammation Induces microRNA-155 Expression in Adipocytes and Adipose Tissue: Outcome on Adipocyte Function[END_REF]) via an induction of PGC1α, which reduces NF-κB activation and transcriptional control of inflammation [START_REF] Eisele | The peroxisome proliferator-activated receptor gamma coactivator 1alpha/beta (PGC-1) coactivators repress the transcriptional activity of NF-kappaB in skeletal muscle cells[END_REF]. The transactivation of other nuclear receptors such as PPARγ by lycopene or its metabolites [START_REF] Ip | Lycopene and apo-10'-lycopenoic acid have differential mechanisms of protection against hepatic steatosis in beta-carotene-9',10'-oxygenase knockout male mice[END_REF] could also be responsible for the anti-inflammatory effect as previously reported [START_REF] Szeles | PPARgamma in immunity and inflammation: cell types and diseases[END_REF].

In conclusion, the impact of lycopene and tomato products on the control of inflammation associated with cardiometabolic disorders is presently well established in vitro and in preclinical studies. Further investigations are mandatory to demonstrate the interest of such nutritional approach in clinical studies. If confirmed, this kind of supplementation could constitute a new strategy to limit or prevent inflammation and its associated consequences.


Sophie Giffard-Roisin

Hervé Delingette

Thomas Jackson

Jessica Webb

Lauren Fovargue

Jack Lee

Christopher A Rinaldi

Reza Razavi

Nicholas Ayache

Maxime Sermesant

Transfer Learning from Simulations on a Reference Anatomy for ECGI in Personalised Cardiac Resynchronization Therapy

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

Transfer Learning from Simulations on a Reference Anatomy for ECGI in Personalised Cardiac Resynchronization Therapy Sophie Giffard-Roisin*, Hervé Delingette, Thomas Jackson, Jessica Webb, Lauren Fovargue, Jack Lee, Christopher A. Rinaldi, Reza Razavi, Nicholas Ayache, Maxime Sermesant*

Abstract-Goal: Non-invasive cardiac electrophysiology (EP) model personalisation has raised interest for instance in the scope of predicting EP cardiac resynchronization therapy (CRT) response. However, the restricted clinical applicability of current methods is due in particular to the limitation to simple situations and the important computational cost. Methods: We propose in this manuscript an approach to tackle these two issues. First, we analyse more complex propagation patterns (multiple onsets and scar tissue) using relevance vector regression and shape dimensionality reduction on a large simulated database. Second, this learning is performed offline on a reference anatomy and transferred onto patient-specific anatomies in order to achieve fast personalised predictions online. Results: We evaluated our method on a dataset composed of 20 dyssynchrony patients with a total of 120 different cardiac cycles. The comparison with a commercially available electrocardiographic imaging (ECGI) method shows a good identification of the cardiac activation pattern. From the cardiac parameters estimated in sinus rhythm, we predicted 5 different paced patterns for each patient. The comparison with the body surface potential mappings (BSPM) measured during pacing and the ECGI method indicates a good predictive power. Conclusion: We showed that learning offline from a large simulated database on a reference anatomy was able to capture the main cardiac EP characteristics from non-invasive measurements for fast patient-specific predictions. Significance: The fast CRT pacing predictions are a step forward to a noninvasive CRT patient selection and therapy optimisation, to help clinicians in these difficult tasks.

Index Terms-Cardiac Electrophysiology, ECG Imaging, Inverse Problem of ECG, Personalisation.

I. INTRODUCTION

H EART failure is a major health issue in Europe affecting 6 million patients and growing substantially because of the ageing population and improving survival following myocardial infarction. The poor short to medium term prognosis of these patients means that treatments such as cardiac resynchronisation therapy (CRT) can have substantial impact [START_REF] Dössel | Inverse problem of electro-and magnetocardiography: Review and recent progress[END_REF], [START_REF] Sutton | Effect of cardiac resynchronization therapy on left ventricular size and function in chronic heart failure[END_REF]. However, these therapies are ineffective in 30% of the treated patients and involve significant morbidity and substantial cost. To this end, the precise understanding of the patient-specific cardiac function can help predict the response to therapy and therefore select the potential candidates and optimise the therapy. S. Giffard-Roisin (sophiegif.github.io), H. Delingette, N. Ayache and M. Sermesant (maxime.sermesant@inria.fr) are with Asclepios Research Group, Université Côte d'Azur, Inria, France.

T. Jackson, L. Fovargue, J. Lee, J. Webb, C. Rinaldi and R. Razavi are with Division of Imaging Sciences and Biomedical Engineering, King's College London, London, UK.

Estimating accurately electrophysiological (EP) patientspecific model parameters is then crucial, and it often involves invasive measurements [START_REF] Sermesant | Patient-specific electromechanical models of the heart for the prediction of pacing acute effects in crt: A preliminary clinical validation[END_REF]. In order to replace these invasive measurements -at risk for the patient-, some studies proposed to personalise the cardiac EP model from body surface potential mappings (BSPM) [START_REF] Li | Localization of the site of origin of cardiac activation by means of a heart-model-based electrocardiographic imaging approach[END_REF]- [START_REF] Dhamala | Spatially-adaptive multi-scale optimization for local parameter estimation in cardiac electrophysiology[END_REF]. In one of them [START_REF] Giffard-Roisin | Noninvasive personalization of a cardiac electrophysiology model from body surface potential mapping[END_REF], the onset activation location and the global conduction velocity were estimated in different pacing locations for several patients using a patient-specific simulated training set. However, personalisation may often be needed in more complex situations, such as multiple activation onsets or heterogeneous myocardial tissue (scar). Besides, such patient-specific methods are time consuming because a large number of model simulations are needed: the total computational time of one model personalisation [START_REF] Giffard-Roisin | Noninvasive personalization of a cardiac electrophysiology model from body surface potential mapping[END_REF] was more than 5 hours on our cluster using parallel computing.

The aim of this article is to develop a reference anatomy model allowing us to perform a common and offline learning. While reducing considerably the computational time of online inference, it also allows to multiply the pathological configurations in the simulated training set as it is built only once. We have thus extended the cardiac EP model personalisation to infarct situations and applied it to a 20 patient database where the BSPM were recorded using the CardioInsight1 jacket now commercially available. The personalised model was then used to predict the activation under different pacing configurations typically used for CRT.

A. EP Model-based Inverse Problem of Electrocardiography

BSPM data has been widely used in the last decades to directly compute the cardiac action potentials by solving an ill-posed inverse problem: finding the transfer matrix linking the torso potentials to the cardiac sources in terms of action potentials or impressed currents [START_REF] Pullan | The inverse problem of electrocardiography[END_REF]. For example, the 3DCEI approach minimizes the use of physiological constraints and was thus applied to various clinical conditions [START_REF] Han | Imaging cardiac activation sequence during ventricular tachycardia in a canine model of nonischemic heart failure[END_REF]- [START_REF] Yu | Three-dimensional noninvasive imaging of ventricular arrhythmias in patients with premature ventricular contractions[END_REF]. Some electrocardiographic imaging (ECGI) methods are integrating physiological and model-based priors in a Bayesian framework [START_REF] Rahimi | Examining the impact of prior models in transmural electrophysiological imaging: A hierarchical multiple-model bayesian approach[END_REF], [START_REF] Wang | Noninvasive computational imaging of cardiac electrophysiology for 3-d infarct[END_REF]. The work by Li and He [START_REF] Li | Localization of the site of origin of cardiac activation by means of a heart-model-based electrocardiographic imaging approach[END_REF] solves the inverse problem by means of heart-model parameters (onset activation location) and was validated with in vivo studies [START_REF] Liu | Estimation of global ventricular activation sequences by noninvasive three-dimensional electrical imaging: Validation studies in a swine model during pacing[END_REF]. It was further developed for localizing PVC origins from convolutional neural networks [START_REF] Yang | Localization of origins of premature ventricular contraction by means of convolutional neural network from 12-lead ecg[END_REF]. With a known onset activation location, the estimation of heterogeneous myocardial conduction using a Bayesian framework has been recently studied by Dhamala et al. [START_REF] Dhamala | Spatially-adaptive multi-scale optimization for local parameter estimation in cardiac electrophysiology[END_REF]. The use of non-invasive personalised cardiac parameters for the prediction of new situations (such as pacing procedures) has been tackled on a few cases only and with a global conduction velocity parameter [START_REF] Giffard-Roisin | Noninvasive personalization of a cardiac electrophysiology model from body surface potential mapping[END_REF].

B. Reference Anatomy in ECGI

These personalisations of EP cardiac model parameters from BSPM data rely on time-consuming patient-specific computations. Because of the natural similarity of the anatomical structures between patients, a reference anatomy can be used.

One study showed the importance of the interindividual variability (averaged standard deviation) of electrocardiograms (ECG) on 25 healthy subjects [START_REF] Hoekema | Interindividual variability of multilead electrocardiographic recordings: influence of heart position[END_REF]. A large part of this variability is due to the heart position and orientation relative to electrodes. In terms of geometry, the larger variations are found for the heart long axis angle. Swenson et al. [START_REF] Swenson | Cardiac position sensitivity study in the electrocardiographic forward problem using stochastic collocation and boundary element methods[END_REF] also revealed the importance of cardiac angulation in the ECG forward problem. Another study showed that ECG imaging is sensitive to global anatomical parameters such as the heart orientation and location with regard to the lead positions [START_REF] Huiskamp | Tailored versus realistic geometry in the inverse problem of electrocardiography[END_REF]. The use of a reference anatomy model, able to represent every patient, is thus a difficult task. Hoekema et al. [START_REF] Hoekema | Interindividual variability of multilead electrocardiographic recordings: influence of heart position[END_REF] showed that by only moving the electrodes in a frontal plane to a common reference, the interindividual variability is not reduced because the heart orientation is not preserved. Another study created a patient-specific adapted torso model by stretching and squeezing a standard torso model according to the measures [START_REF] Lenkova | Individualized model of torso surface for the inverse problem of electrocardiology[END_REF]. They concluded that it was crucial to adapt both the outer shape of the torso model and the position of electrodes according to reality. Yet, it has been also shown that some adapted ventricle-torso standard model were able to get good ECGI results while excluding local geometrical details [START_REF] Wang | How much geometrical detail do we need in cardiac electrophysiological imaging? A generic heart-torso representation for fast subject-specific customization[END_REF], [START_REF] Rahimi | Sensitivity of noninvasive cardiac electrophysiological imaging to variations in personalized anatomical modeling[END_REF]. Lastly, a recent study uses a generic ventricle-torso model in order to build an EP model training set [START_REF] Yang | Localization of origins of premature ventricular contraction by means of convolutional neural network from 12-lead ecg[END_REF], however the training phase had to be patient-specific as the generic geometry was first registered to every patient geometry. To the best of our knowledge, the goals of these geometrical models were only to simplify the anatomical modelling process. However, a study has recently tackled the interindividual variability by separating the factors of variation throughout a deep network using a denoising autoencoder on a large ECG dataset [START_REF] Chen | Disentangling inter-subject variations: Automatic localization of ventricular tachycardia origin from 12-lead electrocardiograms[END_REF] for learning the ventricular tachycardia origin. Inter-subject variations coming from cardiac EP differences and geometry differences are however not separable, so a personalised EP model could not be estimated with this approach.

C. Contributions

The different contributions of this manuscript are:

• A novel reference anatomy approach able to easily represent every patient with preserved heart orientation and position with respect to the lead positions. It reduces considerably the computational cost of the personalisation.

D. Outline of the Manuscript

In the following section II we will present our prediction framework (Figure 1): the clinical data, our reference anatomy model, the simulated EP database and the personalisation of the sinus rhythm sequence. Section III is dedicated to the results and the pacing predictions. Finally, section IV discusses the different aspects of the method.

II. MATERIALS AND METHODS

A. Clinical Data

Our 20 patients dataset is composed of BSPM signals, ventricular myocardial geometry, torso leads and pacing leads locations. All patients have dyssynchrony (either left or right bundle branch block) and were implanted with a biventricular pacemaker (see Table I). The BSPM potentials (from a Car-dioInsight jacket) were acquired at a sampling rate of 1kHz during one QRS complex by 200 to 250 torso sensors. The protocol of this study was approved by the local research ethics committee. The approximated myocardial surface, the location of the torso sensors and the pacing leads were extracted from the 3D CT scanner image. In the stimulation optimisation procedure, cardiologists performed several recordings corresponding to different pacing combinations and delays between a right ventricular (RV) endocardial and a left ventricular (LV) epicardial pacing leads. For almost all patients, a LV pacing alone and a RV pacing alone were performed, together with the 3 following biventricular pacings: simultaneous, LV 40ms (LV lead ahead by 40ms) and RV 40ms (RV lead ahead by 40ms). An atrial pacing was active 200ms to 100ms before the ventricular pacings. A sinus rhythm sequence was also recorded on patients that do not have complete heart blocks. In total, 120 different settings were studied.

B. BSPM Reference Anatomy

1) Transformation to the Reference Anatomy: In this work, every patient p has a geometry data composed of the 3D biventricular cardiac geometry noted c p and s p = {s j p } j=1:N the locations of the N torso sensors. We define a cardiac and BSPM reference anatomy template {c T ; s T } with s T = {s i T } i=1:M onto which every patient data will be transformed.

The current dipole approach formulated in the volume conductor theory [START_REF] Malmivuo | Bioelectromagnetism: principles and applications of bioelectric and biomagnetic fields[END_REF] has proven its efficiency in BSPM calculation [START_REF] Giffard-Roisin | Noninvasive personalization of a cardiac electrophysiology model from body surface potential mapping[END_REF]. The electric potential Ψ v (s j p ) generated by the volume element v and measured at the torso electrode s j p is driven by the scalar product (j v eq , vs j p ) between the equivalent current density j v eq of every cardiac volume element v and the vector directed from v to the torso electrode s j p (further divided by the cubic norm of the distance). Consequently, the shape of the jth BSPM signal Ψ v (s j p ) is closely linked to the direction of vs j p . This result is echoing the conclusions of the ECGI sensitivity studies (see section I-B) showing how the ECG signal is sensitive to the heart orientation and location with respect to the lead positions [START_REF] Huiskamp | Tailored versus realistic geometry in the inverse problem of electrocardiography[END_REF]. First, we propose to rigidly register the cardiac geometry c p to the template c T (it is done interactively, as c p is only a coarse epicardial surface and c T a complete biventricular tetrahedral mesh), and we apply the same transformation to the electrodes s P . We define H as the center of mass of the template cardiac geometry, and Hs i T as the ray from H towards the template torso sensor s i T . We propose the following matching between the template electrodes and the electrodes of a new patient p:

∀i ≤ M, Ψ(s i T ) = Ψ(s j p ) with j = arg min k dist s k p , Hs i T
This matching between electrodes is not bijective. Nevertheless, the advantages of this approach are that measured BSPM signals are not modified and the directional potential is best approximated by identifying the sensor that is the closest to the direction wanted (see Figure 2). This projection, allowing the use of a reference simulated database, can also be seen as a transfer learning method between the reference domain and the patient-specific domain. The final distance dist(s k p , Hs i T ) of the matched electrode to the ray can be a measure of uncertainty: the larger the distance, the larger the uncertainty that may be introduced. The mean distance among the 20 patients was less than 2 cm.

2) Choice of the template: The choice of the template reference anatomy {c T ; s T } is important as c T has to represent the general shape of the myocardium, and the torso sensors s T should be located in relevant positions so that every patient would not be too far from it. In this study, we used a healthy cardiac mesh of 4K vertices and the 251 torso sensors s T from one of our patients having standard torso width and rotation (patient #22, selected manually). One could estimate a mean shape, but for simplicity and consistency reasons we used real geometries. In Figure 3 is shown an example of the matching between an original BSPM signal and its translation to the torso geometry.

C. Offline Simulated Common Database 1) Simulated Database: As we do not have ground-truth intra-cardiac measurements on the 20 patients, it is difficult to learn inter-patient information in order to personalise the EP cardiac model. In order to generate a common large database with detailed cardiac data, we used EP simulations on the reference anatomy to generate 5 000 virtual cases with different parameter values. One simulation runs in approximately 2 minutes on our cluster (CPU core Xeon 2.6GHz). This offline database was used as the training set for all the personalisations of the cardiac EP model, reducing its online computational cost.

2) Forward Electrophysiological Model: On the reference myocardial mesh, the cardiac fiber orientations were estimated with a rule-based method (elevation angle between -70 • to 70 • ). We simulated the anisotropic electrical activation of the heart using the monodomain version of the Mitchell-Schaeffer EP model [START_REF] Mitchell | A two-current model for the dynamics of cardiac membrane[END_REF]. One of the main parameter of the model is the local myocardial conduction velocity c (linked to the diffusion d by cf ∝ √ d. Our forward method is based on a simplified framework composed of sources and sensors in an infinite and homogeneous domain. We modelled every myocardium volume element (tetrahedron) as a spatially fixed but time varying current dipole. We computed simultaneously the cardiac electrical sources and body surface potentials. As shown a related study [START_REF] Giffard-Roisin | Noninvasive personalization of a cardiac electrophysiology model from body surface potential mapping[END_REF], the modelled BSPM signals are similar to the result of a standard boundary elements method, so the unbounded conductor is a valid approximation in this case.

3) Variety of Simulations and Parameter Ranges: In order to simulate a large variability of activation maps and their related BSPM signals, 3 groups of cardiac EP parameters were randomly modified. First, the activation onset location was randomly selected among the endo-and epi-surface vertices of the cardiac mesh. In order to simulate some more complex and realistic situations, an additional second onset location was selected for every simulation [START_REF] Giffard-Roisin | Sparse bayesian non-linear regression for multiple onsets estimation in non-invasive cardiac electrophysiology[END_REF]. Secondly, the global myocardial conduction velocity c was randomly picked in a clinically acceptable range [0.3, 0.7]m/s. Third, in order to capture the conductivity heterogeneity we modelled scar tissue as having no reaction term in the Mitchell-Schaeffer model and a diffusivity reduction of 80%. A varying scar location on the LV with a random and realistic shape [START_REF] Duchateau | Infarct localization from myocardial deformation: prediction and uncertainty quantification by regression from a lowdimensional space[END_REF] was added in 50% of the simulations.

D. Relevance Vector Regression for Sinus Rhythm Personalisation

1) Sinus Rhythm Activation Map Estimation: Using the reference simulated database, we wanted to personalise each patient's EP behaviour from the cardiac at-rest recordings, i.e. the sinus rhythm sequence. For the patients where the sinus rhythm was not available because of complete heart blocks (see Table I), we used the RV pacing sequence. The different parameters (activation onset locations, conductivity, presence of scar tissue) are linked together and their contributions in the resulting BSPM signals are hardly separable. We therefore estimated them at the same time. Because of the large variety of parameters, we chose to regress the whole cardiac activation map (the myocardial depolarisation times) from the BSPM signals. We first described the BSPM signals as a feature vector and we used a dimensionality reduction of the representation of the spatial domain given by the myocardial shape. A relevance vector regression was performed between the BSPM features and the reduced activation maps. The first part of the regression (the training, taking 6 hours to compute on average on our cluster) is common to every patient and was performed offline, while only the second step is patientspecific (the testing, taking 2 minutes to compute on average).

2) BSPM Feature Description: For every patient, the torso sensors were matched with the transformation described in Section II-B1 to the 251 leads of the template torso. Because the reference electrode was not localized, the mean BSPM signal was first subtracted to each signal. Then each signal was normalized and smoothed with a local Gaussian filter. We defined specific features from the QRS sequence of every torso leads. Specifically, 7 features were extracted from each of the 251 QRS signals (figure 4). One BSPM sequence was then represented as the feature vector x i of size L=7x251.

3) Dimensionality Reduction of the Myocardial Shape:

The myocardial tetrahedral mesh can have a large number of elements or vertices. At the same time, the signal to be reconstructed, the activation map, is strongly correlated spatially due to the propagation of the electric potential throughout the myocardium. Therefore, it is meaningful to reduce the dimension of the regression variable, the activation times. A simple way would be to use a coarser mesh but this would be at the expense of reducing the accuracy of the onset locations. Instead, we proposed to use a hierarchical decomposition of the mesh, naturally provided by the eigenmodes of a structural matrix. To this end we chose the eigen-decomposition of the stiffness matrix associated with the Laplacian operator of the tetrahedral shape.

This decomposition has been widely used in various spectral shape analysis [START_REF] Reuter | Laplace-spectra as fingerprints for shape matching[END_REF], [START_REF] Umeyama | An eigendecomposition approach to weighted graph matching problems[END_REF] and is closely related to the modes of vibration of the myocardium. The extracted eigenvectors are naturally sorted by ascending order of spatial frequency. By selecting the first eigenmodes, we only kept the modes with the lowest frequencies corresponding to the largest spatial variations. If we call t the vector of N activation times at each vertex of the myocardial mesh, we get the following reduction and reconstruction formulas:

t red = V M t ; t rec = V M T t red
with t red the coordinates of t in the reduced space, V M the N × M matrix of the first M eigenvectors of the stiffness matrix, and t rec the reconstructed activation times. The matrix V M is independent of t and is thus computed only once. An example of reconstructed activation map (on 4K vertices) using M = 400 modes is shown in Figure 5c. From Figure 5a, we can see that the mean reconstruction error was less than 1.5 ms (max: 8 ms) for 400 modes.

4) Relevance Vector Regression:

In order to regress the myocardial activation times from the BSPM features, we used the relevance vector regression (RVR) method [START_REF] Tipping | Fast marginal likelihood maximisation for sparse bayesian models[END_REF]. This approach will perform a non-linear combination of the training set in order to give a personalised EP estimation. The sparse kernel regression is based on a sparsity inducing prior on the weight parameters within a Bayesian framework. Unlike the commonly used Elastic-Net or Lasso approaches (based on L1 Norm a.k.a Laplacian prior), the RVR method does not require to set any regularization parameters through cross-validation. Instead, it automatically estimates the noise level in the input data and performs a trade-off between the number of basis (complexity of the representation) and the ability to represent the signal. Furthermore, unlike SVM regression or Elastic-Net, it provides a posterior probability of the estimated quantity which is reasonably meaningful if that quantity lies inside the training set cloud of solutions.

The RVR estimates the weights w so that we can predict y ∈ R M (here an activation map in the reduced space) from an input x ∈ R L (here a BSPM feature vector) with a nonlinear relationship between x and y as y = w T Φ(x) where Φ is the non-linear mapping. We consider our dataset of inputtarget pairs {x i , t i } K i=1 where we assume that each target t i represents the true model y i with an addition of a Gaussian noise i = N (0, σ 2 ):

t i = w T Φ(x i ) + i (1)
The complexity of the learned relationship between x and y is constrained by limiting the growth of the weights w. This is done by imposing a zero-mean Gaussian prior on w i :

P (w i ) = N (0, α -1 i ) (2)
where the α i are hyperparameters modifying the strength of each weight's prior. α = {α i } K i=1 and σ are estimated from a marginal likelihood maximisation [START_REF] Bishop | Variational relevance vector machines[END_REF] via an efficient sequential addition and deletion of candidate basis functions (or relevant vectors). Because the optimal values of many α i are infinite, the RVR only selects the BSPM input set that can best explain the activation map in the training set, thus limiting the risk of overfitting. RVR is a multivariate but single-valued approach and therefore the regression was directly performed on the reduced space of section II-D3: only 400 regressions are needed to perform an estimation of more than 4K activation times. In our setting, a training input-target pair corresponds to a BSPM feature vector x i and its related activation map projected on one mode of the reduced space t m red,i . We used Gaussian kernels for the non-linear mapping Φ with a kernel bandwidth of 1e4. The algorithm 2 evolution on the first shape mode (Figure 6) shows a rapid convergence even if small changes in the number of relevant vectors are still visible after 3000 iterations. The mean number of retained relevant vectors during the training phase was 178 (over 5000 training vectors). The testing phase was then performed independently on every patient: from the measured BSPM feature vector x we regressed the activation map estimation t.

5) Local Conduction Velocity Parameter Estimation: the estimated cardiac activation maps obtained from the sinus rhythm sequence were used to retrieve patient-specific conduction velocity (CV) parameters. Because the regression was performed on simulated activation maps, the resulting solution 2 we used a python implementation available at https://github.com/AmazaspShumik/sklearn-bayes is smooth and physiologically relevant. If we consider that a normal heart QRS is less than 120ms, we make the following hypothesis : regions that are late activated during sinus rhythm correspond to regions of slow conduction velocity. This was motivated by the fact that cardiologists are looking at very late activated zones during sinus rhythm for locating scar from CardioInsight inverse solutions. Specifically, we threshold the estimated activation times t a and defined 3 zones: healthy tissue (t a < 120ms, CV = 0.5m/s), damaged tissue (120 < t a < 170ms, CV = 0.3m/s), and scar tissue (170 < t a , no reaction term and diffusion reduced by 80%). We used a single value for the healthy tissue based on a previous study where the personalised global CV were all found close to 0.5m/s [START_REF] Giffard-Roisin | Noninvasive personalization of a cardiac electrophysiology model from body surface potential mapping[END_REF].

6) Pacing Prediction and AV node Activation: We will now predict the activation maps under pacing 'as if' the patient was not implanted yet, using the measured pacing locations from CT imaging and our personalised CV parameters -before comparing with the measured pacing signals. For every patient, the measured ventricular pacing locations were segmented from the CT scanner image, however the image artifacts due to the device only allow an approximate lead location. The personalised parameters from sinus rhythm BSPMs were used to predict the activation maps of different pacing situations. On some patients we found on the CardioInsight inverse solution that the RV was activated without ventricular pacing, probably from the atrial pacing (100 to 200ms before) via the AV node. For these patients (#1, #2 6M, #4, #7, #9, #12, #13) we had to include in our model an AV node activation to the ventricular pacings. For that, we triggered the earliest activated zones estimated in the sinus rhythm result. Because no recording of atrial stimulation and AV delay were available, the triggering time was arbitrarily set to 40ms. We aim at evaluating the regression using a reference anatomy by comparing it with the regression using simulations on the patient-specific torso anatomy, that already showed its efficiency [START_REF] Giffard-Roisin | Sparse bayesian non-linear regression for multiple onsets estimation in non-invasive cardiac electrophysiology[END_REF]. The goal of this previous study was localizing two onset activation locations at the same time from a simulateous pacing using a 1000 patient-specific training set. We show (Figure 7) the lead localization errors of patient #3 from the previous method [START_REF] Giffard-Roisin | Sparse bayesian non-linear regression for multiple onsets estimation in non-invasive cardiac electrophysiology[END_REF] and from the current method (using the reference anatomy, enabling also more training samples:

E. Reference Anatomy Evaluation

-16 -14 -12 -10 -8 -6 -4 -2 -4 - 2

A. Projections on the Reduced Shape Space

In Figure 8 we showed the RVR results of each measured BSPM sequence on the reduced shape space. Because the modes of this space are related to modes of vibration, the results projected on the larger modes allow us to easily compare the BSPM sequences. Each grey point represents one simulated activation map used for training. The measured BSPMs sequences of the 20 patients are shown with colours representing the type of sequence. All the results from the measured BSPM data lie inside the training set point cloud, which is important for the RVR to perform well. We can also see that the 3 different pacing sequences are separated in clusters, with the simultaneous between the RV pacing and the LV pacing. The sinus rhythm results in green are almost all situated near the RV pacing cluster, which is to be expected for LBBB patients. Interestingly, we can notice that the two sinus rhythm exceptions that are closer to the LV pacing group in blue correspond to the two RBBB patients of the cohort (patients #5 and #15).

B. Estimated Sinus Rhythm Activation Maps

The RVR results of the sinus rhythm sequences in terms of activation map were used to estimate the local conduction velocity parameter of each patient. In Figure 9 is represented the mean solution as a transmural activation map (9a) that was compared with the CardioInsight epicardial inverse solution [START_REF] Ramanathan | Noninvasive electrocardiographic imaging (ecgi): application of the generalized minimal residual (gmres) method[END_REF] (9b). The CardioInsight solution is interesting for comparison even if it is only an epicardial surface reconstruction. On top are flat representations of the epicardial surface [START_REF] Soto-Iglesias | Integration of electro-anatomical and imaging data of the left ventricle: an evaluation framework[END_REF]. The wave shape are similar, with a large late activated zone on the lateral LV wall with probable scar tissue. In Figure 9c are shown the retained zones for healthy, diseased and scar tissues from thresholding of Figure 9a. Finally, because the RVR regression provided the result as a Gaussian probability distribution in the reduced shape space, the estimated standard deviation across each mode were projected on the myocardial mesh. The zones with a high estimated standard deviation were found near the valves where the mesh is thin, and the median standard deviation was 37ms (see Figure 9d). The personalisation results of two other patients (#11 and #15) are compared with the CardioInsight solution in Figure 10, showing similar activation maps even for the RBBB pattern (patient #15).

C. Pacing Predictions Results

1) Predicted Activation Maps: From the CV parameters estimated using the RVR solution of the sinus rhythm BSPMs, we ran again our cardiac Mitchell-Schaeffer model by using the measured pacing locations provided by imaging under different conditions (RV only, LV only, simultaneous, LV 40ms, RV 40ms). We compared its output to the measured pacing BSPM recordings and to the CardioInsight solution. This comparison will demonstrate the proximity between a standard inverse method and a predictive method that could be performed without any pacemaker on the patient. In Figure 11 is represented the predicted LV 40ms activation map for patient #9 (Figure 11a), the prediction if we used a model with a homogeneous myocardial CV (Figure 11a) and the CardioInsight solution (Figure 11b). The flat representation allows for a better comparison even if the projection of the epicardium may differ between two cardiac geometries. We can see that the homogeneous CV prediction missed the scar while with the personalised CV the wave shape and timings globally correspond to CardioInsight. The area with 0.2m/s conduction velocity on the LV lateral wall indicates an infarction zone, as also visible on the CardioInsight map.

The prediction of LV only pacing of patient 9 is shown on Figure 12a. The predicted propagation was completely blocked by the scar zone, while an RV activation is visible on the CardioInsight solution Figure 12c. With the AV activation model (see section II-D6), the resulting activation map (Figure 12b) is closer to the CardioInsight solution. In Figure 8, we could see some LV only projections (red) inside the RV only point cloud: they correspond to patients 1, 9 and 12 all showing a separate RV activation and also an important LV late activated near the LV pacing lead. As a quantitative comparison, Figure 14 shows the activation times differences on the flat epicardium, between our predictions and the CardioInsight inverse solutions on 20 patients. The total median difference is 23.8ms. It indicates some similar activation patterns even if few points have an important difference (higher than 50ms). A perfect match is difficult because of the epicardial projections difference, the piece-wise constant CardioInsight solution and the approximation in the pacing electrodes locations. We can notice that the LV only seems to be the more difficult to predict. 2) BSPM predictions: We also predicted the corresponding pacing BSPM signals and compared them with the measured signals. Some signal examples of pacing predictions from patient #3 (Figure 15) showed a clear improvement when using the personalised CV for the LV only, while the homogeneous CV shows already a good agreement for the RV only. In Figure 16 we can see the averaged correlation coefficients (CC) between measured and predicted BSPM signals. Because the cardiac geometry was generic and the pacing locations not accurate, we cannot expect a perfect match between BSPMs. However, we can see that the mean CC of every pacing type increases when the local CV was personalised from sinus rhythm. In particular, the effects on the LV only prediction are highest because the LV damaged tissues can have higher impacts on the wave propagation. We can still see some outliers having low CC values. The lowest one (from patient #16) corresponds to the LV only outlier (red) in the projected modes of Figure 8a, in a zone where the training simulations are sparse. It might indicate that our training set did not cover properly this region of the parameter space.

IV. DISCUSSION

A. Reference Anatomy

The interpatient study could be a useful tool for different applications, as it also allows some comparison between patients. However, it is not universal because our template has a large number of electrodes on both sides of the torso. A new dataset composed of only frontal electrodes would not be correctly projected on the back. The use of a simpler reference with fewer electrodes could be a more general alternative.

We used a reference cardiac geometry, where the size of the heart was fixed. We evaluated the impact of the cardiac scaling on the simulated resulting BSPM signals. Two cardiac scalings of ratios 0.8 and 1.7 to the original size were tested (corresponding to extreme sizes). The center of mass of the myocardium was taken as origin. The resulting normalized BSPM signals showed a relative mean signal difference of 0.1% for the 0.8 scaling ratio and 0.2% for the 1.7 scaling ratio. We can deduce that the size of the heart can be neglected if an appropriate origin is chosen. We did not quantify the error caused by local cardiac shape differences, as the precise patient-specific cardiac anatomy was not available (due to imaging artifacts caused by the pacemaker).

In our setting, the heart location and orientation was segmented from CT scan images. We think that this ionising and computational procedure could be replaced by an estimation of the position and orientation parameters, either by statistical prediction from easy patient characteristics [START_REF] Odille | Statistical variations of heart orientation in healthy adults[END_REF] or by simultaneous EP inverse optimization [START_REF] Rodrigo | Solving inaccuracies in the heart position and orientation for inverse solution by using electric information[END_REF], [START_REF] Coll-Font | Model based approaches to incorporate recordings of multiple heartbeats into the inverse problem of electrocardiography[END_REF].

B. Estimating conduction velocity from activation times

In this work we personalised the local conduction velocity parameter by assigning low values on late activated zones. The direct estimation of local velocity from an activation map raises many challenges (because of the mesh, the anisotropy, the direction of the wave), even though some recent studies are proposing new approaches [START_REF] Doshi | Feasibility of a semi-automated method for cardiac conduction velocity analysis of high-resolution activation maps[END_REF], [START_REF] Duchateau | Spatially coherent activation maps for electrocardiographic imaging[END_REF]. Their use could improve our estimation and thus the predictions, as our translation from activation times into conduction velocity has to be handled with care and might be wrong in some cases.

C. Estimating the uncertainty

The RVR standard deviation is a by-product of the regression and can be a way to interpret the regression uncertainty. However, a proper posterior distribution would be useful for a better accuracy and for identifying the stability of the solution. The use of surrogate modelling into a Metropolis Hasting sampling was recently proposed [START_REF] Dhamala | Quantifying the uncertainty in model parameters using gaussian process-based markov chain monte carlo: An application to cardiac electrophysiological models[END_REF]. We could then also integrate the other sources of uncertainty as the mean torso sensor distance to the template.

D. AV node Activation

We have seen that some patients were activated also from an atrial pacing via the AV node. We have modelled it with an arbitrary time delay, but we think it would be possible to integrate it with more complete data (if the atrial stimulation was recorded). Moreover, the integration of the atria in the ventricular model (for example as a thin layer [START_REF] Schenone | Reduced order models, forward and inverse problems in cardiac electrophysiology[END_REF]) and a study of the whole heart beat ECG could be beneficial for a precise and global personalisation.

V. CONCLUSION

We have developed a methodology for solving the ECG inverse problem and estimating local cardiac conductivity parameters using a physiological model-based regression on a reference anatomy. The data matching to the template anatomy allowed us to use a large offline simulated database of EP forward models for the regression of the BSPM signals from 20 patients with a CRT indication. We used a sparse Bayesian kernel-based regression for the estimation of cardiac activation maps with the use of specific BSPM descriptors and a reduced space for the myocardial geometry. From the CV parameters estimated with the sinus rhythm BSPM sequence, we predicted the responses to different pacing conditions. We compared them with the measured pacing BSPMs and with a commercially available epicardial inverse solution (median activation time difference: 24ms). While a validation with intracardiac recordings is still necessary, we believe that the small patient-specific computational time (less than 2 minutes) can be crucial for a clinical use. We predicted the patientspecific EP response to different pacing configurations, which are useful for the clinician in order to identify CRT responders. It is a first step to an identification of CRT responders from modelling, where we would also need some mechanical output predictions (such as the ejection fraction).

Fig. 1 .

 1 Fig. 1. Fast model-based prediction pipeline: the database composed of 5000 EP simulations and the activation map regression training are common for the 20 patients.

Fig. 2 .

 2 Fig. 2. Reference anatomy: matching between one template torso electrode s i T and the patient electrodes s P . (a) By using a minimal distance between electrodes, the matched electrode would be s 3 P . (b) With the proposed method, the minimal distance to the ray Hs i T indicates a matching to the electrode s 1 P . The directional potential is thus best preserved.

  Fig. 3. Example of measured BSPM signal matching between the torso of patient #1 and the template, the color represents the voltage at one time point from blue (minimal) to red (maximal). (a) original BSPM signal on the torso of patient #1. (b) projected signal using the proposed matching method on the template, in wireframe are the original torso contours.

  red arrow) position of the global extremum, (2: red bar) abs. potential of the global extremum, (3: red sign) sign of the global extremum, (4: blue lines)number of zero crossings, (5: green dots) number of local extrema, (6: blue) algebraic area, (7: green sign) sign of the first extremum.

Fig. 4 .

 4 Fig. 4. Example of BSPM for one torso sensor with the extracted features.

Fig. 5 .

 5 Fig. 5. Example of reconstruction of an activation map (on 4312 vertices) from the eigenvectors of the stiffness matrix: (a) Reconstruction error wrt. the number of modes (b) original activation map (c) reconstructed activation map from 400 modes.

Fig. 6 .

 6 Fig. 6. Iterations of the relevance vector regression training on the first mode.

Fig. 7 .

 7 Fig. 7. Patient #3: Localization error between the previous RVR method and the presented method using the reference anatomy.

Fig. 8 .Fig. 9 .

 89 Fig. 8. RVR results on the reduced shape space from measured BSPM sequences of the 20 patients. Grey dots: projection of the 5000 simulated activation maps used for training.

Fig. 10 .

 10 Fig. 10. Sinus rhythm of two other patients: #11 (a)(b) and #15 (c)(d). Flattening view of the epicardial surface. Left: Estimated activation map from RVR method. Right: CardioInsight epicardial inverse solution.

Fig. 11 .Fig. 12 .Fig. 13 .

 111213 Fig. 11. LV 40ms pacing prediction of patient #9, long-axis and flattening epicardial representations of the activation maps (a) pacing prediction using homogeneous conduction velocity ;(b) using the personalised parameters from sinus rhythm; (c) CardioInsight inverse solution of the actual pacing.

Fig. 14 .Fig. 15 .

 1415 Fig. 14. Prediction of pacing activation maps (20 patients): activation times differences on the flattened epicardial points, between our prediction and the CardioInsight inverse solution. Median difference (red line): 23.8ms.

Fig. 16 .

 16 Fig.[START_REF] Hoekema | Interindividual variability of multilead electrocardiographic recordings: influence of heart position[END_REF]. Prediction of pacing activation maps (20 patients): averaged correlation coefficient (CC) between measured and predicted BSPM signals. In blue, the prediction was performed with a homogeneous myocardial conduction velocity. In red is the result with the CV personalised from sinus rhythm (and with the AV activation in patients 1, 2 6M, 4, 7, 9, 12, 13).

TABLE I CRT

 I PATIENTS. BASELINE INFORMATION OF THE 20 PATIENTS TREATED.

	1	67	M	LBBB	ICM	X
	2	72	M	LBBB	HCM
	2 6M*	72	M	LBBB	HCM	X
	3	82	M	LBBB	NA	X
	4	49	F	LBBB	NICM	X
	5	79	M	RBBB	ICM	X
	6	87	M	LBBB	NICM
	7	62	M	LBBB	ICM	X
	8	87	M	LBBB	ICM	X
	9	77	M	LBBB	NA	X
		69	F	LBBB	NICM
		49	M	LBBB	NICM	X
		62	F	LBBB	NA	X
		59	M	LBBB	ICM	X
		82	F	LBBB	NA
		76	M	RBBB	NICM	X
		55	M	LBBB	NA	X
		49	M	LBBB	NA	X
		78	M	LBBB	NA	X
		73	M	LBBB	NA	X
		71	M	LBBB	NA	X

Id

Age Gen. Block type Etiology SR NA = information not available; SR = sinus rhythm sequence available; LBBB/RBBB = Left/Right bundle branch block; ICM/NICM/HCM = ischemic/non-ischemic/hypertrophic cardiomyopathy; *: patient #2 was acquired 2 times with a 6months follow-up.

ECVUE, CardioInsight Technologies Inc., Cleveland, Ohio

ACKNOWLEDGMENT

The research leading to these results has received European funding from the Seventh Framework Programme (FP7/2007-2013) under grant agreement VP2HF n°611823 and ERC starting grant ECSTATIC (715093).