Mahdi Gueffaz
email: mahdi.gueffaz@u-bourgogne.fr

Sylvain Rampacek
email: sylvain.rampacek@u-bourgogne.fr

Christophe Nicolle
email: cnicolle@u-bourgogne.fr

RDF2NµSMV: Mapping Semantic Graphs to NµSMV Model Checker

Keywords: Semantic graph, RDF, Model-checking, temporal logic, NµSMV, IFC, BIM

The most frequently used language to represent the semantic graphs is the RDF (W3C standard for meta-modeling). The construction of semantic graphs is a source of numerous errors of interpretation. The processing of large semantic graphs is a limit to the use of semantics in current information systems. The work presented in this paper is part of a new research at the border between two areas: the Semantic Web and the model checking. For this, we developed a tool, RDF2NµSMV, which converts RDF graphs into NµSMV language. This conversion aims checking the semantic graphs with the model checker NµSMV in order to verify the consistency of the data. To illustrate our proposal we used RDF graphs derived from IFC files (Building Information Modeling). These files represent digital 3D building model. Our final goal is to check the consistency of the IFC files that are made from a cooperation of heterogeneous information sources (plumbers, architects, electricians, etc.)

I. INTRODUCTION

The increasing development of networks and especially the internet has greatly developed the heterogeneous gap between information systems. In glancing over the studies about interoperability of heterogeneous information systems we discover that all works tend to the resolution of semantic heterogeneity problems. Now, the W3C (World Wide Web Consortium) suggests norms to represent the semantic by ontology. Ontology is becoming an inescapable support for information systems interoperability and particularly in the Semantic Web. Literature now generally agrees on the Gruber's terms to define an ontology: explicit specification of a shared conceptualization of a domain [START_REF] Gruber | Toward principles for the design of ontologies used for knowledge sharing. Presented at the Padua workshop on Formal Ontology[END_REF]. The physical structure of ontology is a combination of concepts, properties and relationships. This combination is also called a semantic graph.

Several languages have been developed in the context of Semantic Web and most of these languages use XML (eXtensible Markup Language) as syntax [START_REF] Bray | Extensible Markup Language (XML) 1.1 (second edition) W3C recommendation[END_REF]. The OWL (Web Ontology Language) [START_REF] Bechhofer | OWL Web Ontology Language Reference[END_REF] and RDF (Resource Description Framework) [START_REF] Becket | RDF/ XML Syntax Specification (Revised)[END_REF] are the most important languages of the semantic Web, they are based on XML. OWL allows representing the ontology, and it offers large capacity machines performing web content. RDF enhances the ease of automatic processing of Web resources. The RDF (Resource Description Framework) is the first W3C standard for enriching resources on the web with detailed descriptions. The descriptions may be characteristics of resources, such as author or content of a website. These descriptions are metadata. Enriching the Web with metadata allows the development of so-called Semantic Web [START_REF] Berners-Lee | The Semantic Web[END_REF]. The RDF is also used to represent semantic graph corresponding to a specific knowledge modeling. For example in the AEC (Architecture Engineering Construction) projects, some papers used RDF to model knowledge from heterogeneous sources (electricians, plumbers, architects, etc.). In this domain, some models are developed providing a common syntax to represent building objects. The most recent is the IFC (Industrial Foundation Classes) [START_REF]Industrial Foundation classes, International Alliance for interoperability[END_REF] model developed by the International Alliance of Interoperability. The IFC model is a new type of BIM (Building Information Model) and requires tools to check the consistency of the heterogeneous data and the impact of the addition of new objects into the building.

As the IFC graphs have a large size, their checking, handling and inspections are a very delicate task. In [START_REF] Vanland | IFC and Buildings Lifecycle Management[END_REF] we have presented a conversion from IFC to RDF. In this paper, we propose a new way using formal verification, which consists in the transformation of semantic graphs into a model and verifying them with a model checker. We developed a tool called "RDF2NµSMV" that transforms semantic graphs into a model represented in NµSMV [START_REF] Cimatti | NuSMV: a new symbolic model checker[END_REF] language. After this transformation, NµSMV verifies the correctness of the model written in NµSMV language with temporal logic in order to verify the consistency of the data described in the model of the huge semantic graphs.

The rest of this paper is organized as follows. In Section 2 we present an overview of the semantic graphs, especially the structure of the RDF graphs and the model checking. Then, in Section 3, we describe the mapping of the semantic graphs into models and our approach is defined in Section 4. Finally, we end with the conclusion.

II. AN OVERVIEW OF SEMANTIC GRAPHS AND MODEL

CHECKING

The RDF is also used to represent semantic graphs corresponding to a specific knowledge modeling. It is a language developed by the W3C to bring a semantic layer to the Web [START_REF] Klyne | Resource Description Framework (rdf): Concepts and abstract syntax[END_REF]. It allows the connection of the Web resources using directed labeled edges. The structure of the RDF documents is a complex directed labeled graph. An RDF document is a set of triples <subject, predicate, object> as shown in the Figure 1. In addition, the predicate (also called property) connects the subject (resource) to the object (value). Thus, the subject and the object are nodes of the graph connected by an edge directed from the subject towards the object. The nodes and the edges belong to the "resource" types. A resource is identified by an URI (Uniform Resource Identifier) [START_REF] Bönström | Storing RDF as a graph[END_REF][START_REF] Berners-Lee | W3C recommandation[END_REF]. The declarations can also be represented as a graph, the nodes as resources and values, and the arcs as properties. The resources are represented in the graph by circles; the properties are represented by directed arcs and the values by a box (a rectangle). Values can be resources if they are described by additional properties. For example, when a value is a resource in another triplet, the value is represented by a circle. The RDF graph in the fig. 2 defines a node "University of Bourgogne" located at "Dijon", having as country "France" and as department "Cote d'Or". RDF documents can be written in various syntaxes, e.g., N3 [START_REF] Berners-Lee | Notation3 (N3): A readable RDF syntax[END_REF], N-Triple [START_REF] Becket | RDF test cases. W3C Working draft[END_REF], and RDF/XML. Below, we present the RDF\XML document corresponding to Figure 2. The model checking [START_REF] Katoen | Principles of Model Checking[END_REF] described in fig. 3 is a verification technique that explores all possible system states in a bruteforce manner. Similar to a computer chess program that checks all possible moves, a model checker, the software tool that performs the model checking, examines all possible system scenarios in a systematic manner. In this way, it can be shown that a given system model truly satisfies a certain property. Even the subtle errors that remain undiscovered using emulation, testing and simulation can potentially be revealed using model checking.

To make a rigorous verification possible, properties should be described in a precise unambiguous way. It is the temporal logic that is used in order to express these properties. The temporal logic is a form of modal logic that is appropriate to specify relevant properties of the systems. It is basically an extension of traditional propositional logic with operators that refer to the behavior of systems over time. The following algorithm explains the way that the model checking works. First we put in the stack all the properties expressed in the temporal logic. All of them are verified one by one in the model and if a property does not satisfy the model, it is whether the model or the property that we must refine. In case of a memory overflow, the model must be reduced. Whereas formal verification techniques such as simulation and model checking are based on model description from which all possible system states can be generated, the test, that is a type of verification technique, is even applicable in cases where it is hard or even impossible to obtain a system model.

III. THE MAPPING

The RDF graphs considered here are represented as XML verbose files, in which the information is not hierarchically stored (so-called graph point of view). These RDF graphs are not necessarily connected, meaning they may have no root vertex from which all the other vertices are reachable. The RDF graph transformation into a model is articulated in three steps: exploring the RDF graph, holding election of the root vertex, generating the model of the semantic graph.

A. Exploring RDF graph

In order to exploit the RDF graphs, we therefore have to determine whether they have a root vertex, and if this is not the case, we must create a new root vertex by taking care to keep the size of the resulting graph as small as possible.

We achieve this by appropriate explorations of the RDF graphs, as explained below. Let us consider that an RDF graph is represented as a couple (V, E), where V is the set of vertices and VxV E  is the set of edges. For a vertex x, we note

  E y x V y x E    ) , ( ) (
the set of its successor vertices, and we assume that these vertices are ordered from 0

) (x E to 1 ) ( ) (  x E x E
. This corresponds to the classical data structure for representing graphs in memory, consisting of an array indexed by the vertices and containing in each entry the list of successor vertices of the corresponding vertex. There are several algorithms to traverse a large graph, of these basic algorithms include the best known, depth-first search (DFS) and breadth-first search (BFS). We use depth-first search algorithm illustrated below to explore graph, knowing that the breadth-first algorithm also works in this context: We considered here an iterative variant of DFS, which makes use of an explicit stack, rather than the recursive variant given in [START_REF] Tarjan | Depth-First search and linear graph algorithm[END_REF]; this is required in practice to avoid overflows of the system call stack when the algorithm is invoked for exploring large graphs.

B. Determining a root vertex

the RDF graph has no vertex root, we must create a root as to be the successors of all vertices of the graph but it will increase the number of edges. We look forward to doing this by adding a few edges as possible. A vertex x of a directed graph is a partial root if it cannot be reached from any other vertex of the graph. If the graph contains only one partial root, all other vertices of the graph can be reached from the root, otherwise there would be other roots in the partial graph. If the graph has multiple partial roots, the most economical way to provide a root is to create a new record with all the roots as a partial successor: this will add to the graph a minimum number of edges. We compute the set of partial roots in two phases, each one consisting in successive explorations of the graph. The first phase identifies a set of candidate partial roots, and the second one refines this set in order to determine the partial roots of the graph.

Remark: a property must always have a resource and a value; the resource should never be a value with the same predicate, i.e. a loop in the graph. The first phase explores the graph until it is fully explored, and inserts in root_list all vertices that have no predecessor. If root_list contains a single vertex, so overall it is the global root of the graph since all the other vertex are accessible from it and it is useless to the second phase has passed. Otherwise, any vertex contained in root_list could also be a root of the graph: the role of the second phase is to determine which of the partial root the root of the global graph is.

The second phase performs a new wave of exploration of the roots contained in partial root_list in reverse order in which they were inserted in the list. If a root in root_list is to be visited by a partial root, it is removed from the list because it is not a partial root. At the end of this phase, all partial roots of the graph are present in root_list. Indeed, each vertex is unreachable from the partial roots which were explored during the second phase. A new root is created (see Fig. 4), having as successor all the partial roots of root_list, which ensures that all vertices of the graph are accessible from the new root. Therefore, such a summit is inaccessible from other nodes of the graph.

A B C R A B C Figure 4.
A root is a single node that has no predecessor. In this graph, we node A and node B, two roots, and then we will create a new virtual root (blue circle "R") that points to the two roots.

The algorithm for determining a root has a complexity O(|V|+|E|), linear in the size of the graph (number of vertices and edges), since each phase visits every state and traverses every edge of the graph only once. Given that the graph must be traversed entirely in order to determine whether it has a root or not, this complexity is optimal.

C. Generating the model

The third step is divided into three sub-steps. The first one consists in creating the table of all triplets by exploring the entire graph; the second one consists in generating the correspondence table and the last one in producing the model representing the semantic graph in NµSMV language.

Table of triplets -Going through the RDF graph by graph traversal algorithms, we will create a table consisting of resources, properties and values. In our RDF graph, the resource is a vertex, the property represents the edge and the value is the successor vertex corresponding to the edge of the vertex. The table of triplets of the RDF graph is useful to the next sub-step.

Correspondence table -In this second sub-step, RDF2NµSMV generates a table of correspondence. This table contains an identifier for each resource, property and value.

The model -In this last sub-step, we will write the model in NµSMV language for RDF2NµSMV tool, corresponding to the RDF graph that we want to check.

IV. THE VERIFICATION WITH MODEL CHECKER

As we saw in Section 2, the model checker needs properties in order to check the model of semantic graphs. These properties are expressed in temporal logic. The concepts of temporal logic used for the first time by Pnueli [START_REF] Pnueli | The temporal logic of programs[END_REF] in the specification of formal properties are fairly easy to use. The operators are very close in terms of natural language. The formalization in temporal logic is simple enough although this apparent simplicity therefore requires significant expertise. Temporal logic allows representing and reasoning about certain properties of the system, so it is well-suited for the systems verification. There are two main temporal logics, that is linear time and branching time. In linear time temporal logic, each execution of the system is independently analysed. In this case, a system satisfies a formula f, if f holds along every execution. The branching time combines all possible executions of the system into a single tree. Each path in the tree is a possible representation of the system execution. This Section details our approach which consists in transforming semantic graphs into models in order to be verified by the model-checker. For this, we have developed a tool called "RDF2NμSMV" that transforms semantic graphs into NµSMV language.

We use NµSMV as model checkers to verify the model of semantic graphs. NµSMV is the amelioration of SMV model checker, working on the same simple principles as SMV. NµSMV verifies the properties in both linear time logic and computation tree logic. The architecture of the fig. 5 is divided into two phases. The first phase concerns the transformation of the semantic graph into a model using our tool "RDF2NμSMV", as described in Section III. The second phase concerns the verification of the properties expressed in temporal logic on the model using the model-checker NµSMV.

To illustrate our approach, we take an RDF graph represented in the Figure 6 and a temporal logic expressed in the table 1 to verify if the BIM "b1" contains a floor.

True

We tested several RDF graphs on our tool "RDF2NµSMV", graphs representing buildings as shown in Figure 7, using a machine that runs on a processor with a capacity of 2.4 GHz and 4 GB of RAM, calculating the time of conversion as shown in Fig 8. Note that the RDF2NµSMV tool is faster in converting semantic graphs. We have almost 22 seconds for a graph of 53 MB size. The transformation tool follows a polynomial curve. In Fig. 9, we see the size of the converted semantic graphs from RDF to NµSMV language. V. CONCLUSION This paper presents how to transform a semantic graph into a model for verification by using a powerful formal method, that is the "model checking". Knowing that the model-checker does not understand the semantic graphs, we developed a tool RDF2NµSMV to convert them into NµSMV language in order to be verified with the temporal logics. This transformation is made for the purpose of classifying large semantic graphs in order to verify the consistency of IFC files representing 3D building. We notice the advantage of NµSMV, whose verification can be made with both linear time logic and computation tree logic formulas.

Figure 1 .

 1 Figure 1. RDF triplet.

Figure 2 .

 2 Figure 2. Example of a partial RDF graph.

  <rdf:Description rdf:about="http://example.org/university of Bourgogne"> <ex:Location> <rdf:Description rdf:about="http://example.org/Dijon"> <ex:Country> France</ex:Country> <ex:Department>Cote d'or</ex:Department> <

Figure 3 .

 3 Figure 3. Model Checking approach

  Algorithm: procedure Dfs (x): begin visited(x) := true; // vertex x becomes visited p(x) := 0; // start exploring its successors stack := push(x, nil); while stack ≠ nil do y := top(stack); if p(y) < |E (y)| then // y has some unexplored successors z := E (y) ) := p(y)+1; // take the next successor of y if  visited (z) then visited(z) := true; // visit it p(z) := 0;//start exploring its successors stack := push(z, stack) endif else //all successors of y were explored stack := pop(stack) endif end end

  Algorithm: procedure RootElection(): // precondition:  x  V.visited(x) = false Begin // first phase root_list := nil; forall x  V do if  visited(x) then Dfs(x); root_list := cons(x, root_list) single partial root is the global root else forall x  V do visited(x):= false; endfor; forall x  root_list do // reexplore partial roots in reverse order if  visited(x) then Dfs(x) else root_list := root_list \ {x} // partial root is not a real one endif endfor; if |root_list| = 1 then root := head(root_list) // a single partial root is the global root else root := new_node(); // new root predecessor of the partial roots E(root) := root_list endif endif

Figure 5 .

 5 Figure 5. Our architecture.

Figure 6 .

 6 Figure 6. Example of partial RDF graph.

Figure 7 .

 7 Figure 7. The 3D view of an IFC file.

Figure 8 .

 8 Figure 8. Time conversion of semantic graphs.

Figure 9 .

 9 Figure 9. Size of the models

TABLE 1 .

 1 Temporal logic formula.

	First phase
	M: semantic graph
	(RDF)
	RDF2NµSMV
	M': simplified model	Temporal logic
	of semantic graph	description of
		semantic graph
		Tool verification
	Second phase	Model-checker (NµSMV)
	M' satisfies the	M' not satisfies +
	temporal logic	counter example

http://example.org/University_of_Bourgogne http://example.org/Dijon http://example.org/Cote_d'or http://example.org/France http://example.org/Location http://example.org/Country http://example.org/Department


Dr Maxime Guye
email: maxime.guye@ap-hm.fr

Gaelle Bettus

Fabrice Bartolomei

Sylviane Confort-Gouny

Eric Guedj

; Patrick Chauvel

; Patrick

J Cozzone

Jean-Philippe Ranjeva

; Maxime Guye

ROLE OF RESTING STATE FUNCTIONAL CONNECTIVITY MRI IN PRESURGICAL INVESTIGATION OF MESIAL TEMPORAL LOBE EPILEPSY

Keywords: Functional connectivity, temporal lobe epilepsy, resting state fMRI

Objective: We aimed at determining the ability of resting-state functional connectivity MRI (fcMRI) to lateralize/localize the epileptogenic zone in patients presenting with mesial temporal lobe epilepsy (MTLE) at the individual level.

ROIs in each hemisphere) usually involved in epileptogenic networks of MTLE. Normalized correlation coefficients between pairs of ROIs signal time-courses were computed to reflect BFC. Based on normative BFC values obtained from 36 controls, the number of BFC decreases and increases were determined in each hemisphere for each patient.

Results: BFC decreases were found bilaterally although the number of decreased links was significantly higher in the epileptogenic side (p=0.025). Conversely, BFC increases were found almost exclusively in the contralateral lobe leading to a strong test effect for locating the nonepileptic lobe with a sensitivity of 64% and a specificity of 91% (p<0.001). The most frequently disconnected areas were the entorhinal cortex and the anterior hippocampus in the epileptic lobe, while contralateral BFC increases involved preferentially hippocampus and amygdala.

Conclusions:

This study demonstrates that the presence of BFC increases in the non-epileptic side was paradoxically the most specific marker of epileptogenic zone localization; and suggests that a single resting-state fcMRI could be useful in the presurgical assessment of MTLE at an individual level.

INTRODUCTION

Since the early 90s, fMRI has been widely used as a mapping tool of brain function by measuring task-related BOLD variations. During the last decade, the neuroimaging field has witnessed a dramatic evolution of fMRI after the discovery of spontaneous fluctuations of BOLD signal at rest exhibiting coherent variations in regions belonging to the same functional networks 1, 2 . Such coherent slow frequency fluctuations are at the basis of functional connectivity MRI (fcMRI) measurements and have contributed to bring new insights into the complex brain network organisation [START_REF] Van Geit | Neurofitter: a parameter tuning package for a wide range of electrophysiological neuron models[END_REF][START_REF] Guye | Imaging structural and functional connectivity: towards a unified definition of human brain organization?[END_REF] . Moreover, the clinical impact of neurological disorders may be related to disorganization of such complex networks, justifying the need for non-invasive characterization of functional connectivity.

In mesial temporal lobe epilepsy (MTLE), using fcMRI it has already been demonstrated that cognitive impairment associated with TLE may be linked to specific cognitive network disturbances. In particular, Waites et al. showed that language impairment was related to functional connectivity decrease between regions belonging to the language network previously defined by task-related fMRI [START_REF] Waites | Functional connectivity networks are disrupted in left temporal lobe epilepsy[END_REF] . In addition, using fcMRI during a memory task, Addis et al. demonstrated functional disconnections of autobiographical memory network in MTLE patients with hippocampal sclerosis (HS) [START_REF] Addis | Consequences of hippocampal damage across the autobiographical memory network in left temporal lobe epilepsy[END_REF] . Such studies are in favour of a relationship between altered functional connectivity and cognitive dysfunctions associated with MTLE. However, although such functional networks are closely related/connected to the epileptogenic zone (EZ, i.e. responsible for seizure onset), they do not correspond to the EZ itself that mainly involves mesial temporal lobe structures.

Nonetheless, in drug-resistant partial epilepsies, the main goal of epilepsy surgery is to locate and remove the EZ. That is why in a recent study, using resting-state fcMRI, we studied the capability of fcMRI to define functional connectivity alterations in the EZ [START_REF] Bettus | Decreased basal fMRI functional connectivity in epileptogenic networks and contralateral compensatory mechanisms[END_REF] . In this previous study, a homogeneous group of left MTLE patients was investigated and basal functional connectivity (BFC) decreases were found within the EZ, associated with a concomitant BFC increase in contralateral lobe at the group level. However, the stringent selection criteria and the restricted number of patients did not allowed to answer the question of the clinical relevance of BFC alterations from an individual point of view which was addressed in the present work. Here, the main goal was to define the individual usefulness of resting state fcMRI in the presurgical evaluation of drug-resistant TLE. To this aim, we studied a larger and less selected (in term of handedness and epilepsy laterality) cohort of patients (n=22) compared to controls (n=36), and determined the sensitivity and specificity of altered connectivity as a test to lateralize/localize the epileptogenic network at the individual level. We also sought to determine individually, the extent, the types and the significance of such functional connectivity alterations.

SUBJECTS/MATERIALS AND METHODS

Subjects

Fifty-eight subjects (22 patients and 36 controls) gave their informed consent to be included in this study approved by the local Ethics Committee of Marseille Public Hospital.

The 22 patients (10 with right MTLE, 5 females; and 12 with left MTLE, 7 females) were selected after a comprehensive non-invasive pre-surgical evaluation. Though exhibiting different types of pathology on conventional MRI, all other data (i.e. interictal and ictal video-EEG (especially ictal electro-clinical semiology), interictal FDG-PET, interictal +/-ictal SPECT were compatible with a seizure onset in the mesial temporal lobe structures. Thus, as defined in a previous work using SEEG [START_REF] Maillard | Semiologic and electrophysiologic correlations in temporal lobe seizure subtypes[END_REF] . we selected these patients as they presented homogeneous electroclinical features compatible with MTLE . In the two groups, handedness was controlled (for patients: left-handed n=6, age=40 ±11 and right-handed n=16, age=38 ±13; with no age differences between the two groups: Kruskal-Walis p=0.740; for controls left-handed n=10, age=30 ±11 and right-handed n=26, age=29 ±9; with no differences between the two groups: Kruskal-Walis p=0.791).

Clinical characteristics of MTLE patients are reported in

Neuropsychological testing

Normative evaluation of memory capacity and intelligence scale of patients were assessed by the

Resting state fcMRI

Data acquisition and processing

Two hundred brain volumes (time elapsed between blocks: 4s) were acquired using a single-shot multislice gradient-echo echo-planar imaging (GE-EPI) sequence (TE 55ms, TR 4s, 30 contiguous slices, 4mm-thickness, matrix 64, FOV 256mm). Subjects were instructed to simply keep their eyes closed and to not fall asleep.

Data processing

Resting-state fcMRI acquisitions were pre-processed using SPM2 software (Welcome Institute, London, UK). After slice timing correction, images were realigned before spatial normalization (16 non linear registration 7x6x7 basis functions) and smoothing (12 mm). Sources of spurious or regionally non-specific variance related to physiological artefacts were removed by using a method previously described [START_REF] Bettus | Decreased basal fMRI functional connectivity in epileptogenic networks and contralateral compensatory mechanisms[END_REF][START_REF] Bartels | The chronoarchitecture of the cerebral cortex[END_REF][START_REF] Vincent | Coherent spontaneous activity identifies a hippocampal-parietal memory network[END_REF] . We used a regression including the CSF signal (averaged over the lateral ventricles) and the white matter signal (averaged over a region centered in the deep cerebral WM) in order to reduce the non-neuronal contributions to BOLD correlations [START_REF] Bettus | Decreased basal fMRI functional connectivity in epileptogenic networks and contralateral compensatory mechanisms[END_REF][START_REF] Bartels | The chronoarchitecture of the cerebral cortex[END_REF][START_REF] Vincent | Coherent spontaneous activity identifies a hippocampal-parietal memory network[END_REF] .

Regions of interest

According to the methodology described in a previous study 7 , 10 ROIs (5 in each hemisphere) usually involved in epileptogenic networks of mesial TLE were defined. These regions were automatically obtained from a digital Talairach atlas (Pick atlas toolbox, SPM2) and consisted of amygdala (Amy), entorhinal cortex (EC), anterior hippocampus (AntHip), posterior hippocampus (PostHip) and temporal pole (Brodmann area 38, BA38) [START_REF] Bettus | Decreased basal fMRI functional connectivity in epileptogenic networks and contralateral compensatory mechanisms[END_REF] . These ROIs were used as masks applied onto the residual images to extract the mean signal time-courses from each predefined ROI.

To determine functional interactions between ROIs in each temporal lobe, correlation coefficients between pairs of signal time-courses were computed (JMP statistical software).

Correlation coefficients were then normalized using the Fisher transformation (rN = 0.5 Log

[(1+r)/(1-r)]
) to reflect BFC and to perform subsequent statistical analyses.

.

Normative BFC values in controls

For each control group (left and right-handed), unilateral BFC values were tested against the null hypothesis for each link (Mann Whitney test, corrected-p<0.005 (10 comparisons per group)) to determine the statistical significance of each correlation. BFC values for non-significant correlations were set to zero. Within-group effect of BFC was tested for left and right-handed controls group for each link (Wilcoxon signed rank).

For each subgroup, differences in BFC values between contralateral links were also tested (Wilcoxon rank test, corrected p<0.005, (10 comparisons per group)).

For each hemisphere, between-group (left handed and right handed controls) comparisons of BFC values were performed for each link using a Mann Whitney test. A corrected p<0.005 was considered to account for the 10 comparisons.

BFC at individual level in patients

In order to evaluate the added value of BFC at the individual level, z scores were calculated for patients relative to the mean and standard deviation of the corresponding subgroup of controls.

To limit the potential bias related to possible variations in structural and functional asymmetries of temporal lobes, we calculated z-scores for patients relative to control group with the same handedness. Each connection from each hemisphere were classified as abnormal for z score values above or below 2 for each subject and the number of BFC decreases and increases were obtained for each hemisphere of each patient. In controls, this cutoff of 2 SD included the percentile 90 for each link studied and for the two groups of controls (right and left handed).

Sensitivity and specificity of BFC alterations to locate the epileptogenic hemisphere

Two types of connectivity alterations were evaluated: i) the presence of at least one link showing significant BFC decrease relative to controls in the epileptogenic temporal lobe, ii) the presence of at least one link with significant BFC increase relative to controls in the contralateral temporal lobe.

For these two criteria, we classified BFC decreases and increases from each hemisphere of each patient as true positive, true negative, false positive and false negative. Then, we assessed: sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and the power of these tests using Yule's Q coefficient, χ² and Yuden's index. The two criteria tested to classify the epilepsy lateralization were intentionally very simple and were defined a priori. No attempt to optimize these criteria was conducted on the present dataset to limit artificial overfitting.

Relationships between BFC and clinical data

In patients, pair-wise correlations were performed in order to determine a potential relationship between BFC and : i) neuropsychological scores; ii) seizure frequency; and iii) disease duration.

We considered that each test aimed at answering a single different question; consequently no multiple comparison corrections were applied (Spearman Rho, p<0.05).

RESULTS

Neuropsychological data in patients

Neuropsychological data are reported in Table2.

Normative BFC in controls

In right-handed controls, significant BFC was present bilaterally for all the possible links (p<0.05) except for the link EC-PostHip in the two hemispheres. Left-right asymmetry in BFC was present, with a left predominance for the links AntHip-PostHip, Amy-AntHip and Amy-PostHip, while a right predominance was observed for the link Amy-EC (p<0.005).

The same significant links were observed in left handed and right handed controls. Left-right asymmetry in BFC was present with a left predominance for the link Amy-AntHip, while a right predominance was observed for the link Amy-EC (p<0.005).

Relative to the left-handed group, the right-handed controls showed BFC increase in the left temporal lobe for the links Amy-PostHip and AntHip-PostHip (p<0.005) (Figure 1). Group data are reported in Table 3).

Accordingly, individual results in patients were compared to the subgroup of controls with the same handedness.

Altered BFC in patients

Individual data are reported in Table 4. that in these two patients the number of links with BFC increase was the same or higher in the lobe contralateral to seizure.

At the group level, the number of links with BFC decrease was higher in the epileptogenic side (mean=1.545, SD=1.262) compared to the contralateral hemisphere (mean=0.727, SD=0.935) (p=0.025) (Figure 2 a). Moreover, the number of links with BFC increase was higher in the nonepileptogenic temporal lobe (mean=1.091, SD=1.192) in comparison with the epileptogenic side (mean=0.091, SD=0.294) (p<0.001), (Figure 2 b).

Qualitative study of the most altered areas

The most disconnected structure of the epiletogenic network studied was the EC. Indeed, in the whole group of patients, we observed 25 links with significant BFC decreases involving EC in the EZ (36.76% of links with BFC decreases). The second most altered area was the AntHipp (16; 23.53%).

In the contralateral temporal lobe, BFC increases most often involved PostHipp (16; 33.60%), AntHipp (11; 22.91%) and Amy (10; 20.83%).

Diagnostic value of BFC to lateralize epilepsy (Table 5)

Table 5 shows the sensitivity, specificity, PPV and NPV for each test.

BFC decreases were found bilaterally exhibiting a lateralization power with a sensitivity of ~73% and a specificity of 54.5%. Conversely, BFC increases were found almost exclusively in the contralateral lobe leading to a strong test effect for locating the non-epileptic lobe with a sensitivity of 64% and a specificity of 91% (p<0.001).

a)

Diagnostic

Relationship between BFC and visible lesions on conventional MRI

Thirteen patients had unilateral HS; 4 had cortical dysplasia and 2 had other types of lesions (1 temporal post-traumatic lesion and 1 hippocampal dysplasia 1). We tested (Wilcoxon rank test)

the influence of MRI visible lesion on neuropsychological data and the number of altered links, by comparing data from the subgroup of 13 patients with HS and the subgroup of 9 patients with no HS. No difference was observed for neuropsychological data between HS and no HS subgroup. In addition no significant difference was found between the two groups concerning the number of altered BFC, neither the number of BFC decreases in the EZ lobe nor the number of BFC increases in the contralateral lobe.

Relationships between BFC and clinical data

No significant statistical relationship was observed between seizure frequency, disease duration and BFC. However, correlating BFC values with neuropsychological memory scores in patients, we observed a significant positive correlation between working memory (WM) scores and increased BFC in the non-epileptogenic temporal lobe. Indeed, the Amy-BA38 link, contralateral to EZ, was correlated with working memory quotient (WMQ) (Spearman Rank test, p=0.0494) and auditory WMQ (p=0.0413). In addition, the EC-AntHipp link contralateral to EZ, was positively correlated with WMQ (p=0.0109), auditory WMQ (p=0.0043) and visual WMQ (p=0.0024). We also tested the spread between verbal and visual performance scores as a potential parameter. However, we did not found any significant discrepancies between visual and auditory subtests in patients (p=0.765 in right sided MTLE, and p=0.680 in left sided MTLE using a paired test (Wilcoxon test)).

DISCUSSION

Using resting state fcMRI we studied BFC in patients presenting with MTLE in order to test the accuracy of the method in the EZ location.

In this work, the main results were: (i) presence of a BFC alteration pattern characterized by a bilateral decrease predominant in the epileptic side, and an unilateral increase almost exclusively observed in the contralateral "non-epileptic" lobe, (ii) evidence that the most frequently disconnected areas were the entorhinal cortex and the anterior hippocampus in the epileptic lobe, while contralateral BFC increases involved preferentially posterior hippocampus, anterior hippocampus and amygdala, (iii) at the individual level, presence of BFC increases in the nonepileptic (contralateral) side was paradoxically the most specific marker of EZ localization .

Basal functional MTL connectivity lateralization depending on handedness

In right-handed controls, an asymmetrical leftward organization of BFC was present between MTL structures. These results were discussed in a previous study [START_REF] Bettus | Decreased basal fMRI functional connectivity in epileptogenic networks and contralateral compensatory mechanisms[END_REF] in line with structural data obtained by quantitative diffusion tensor tractography [START_REF] Nucifora | Leftward asymmetry in relative fiber density of the arcuate fasciculus[END_REF] . Compared to right-handed controls, left-handed controls BFC was less leftward lateralized, except for amygdala-anterior hippocampus link. This interesting finding clearly showed an effect of handedness on BFC in controls. Effect of handedness on temporal lobes volumes has already been reported in studies using morphometry [START_REF] Herve | Handedness and cerebral anatomical asymmetries in young adult males[END_REF] . However, to our knowledge, no study has investigated handedness effect on BFC, and resting-state connectivity studies include preferentially right-handed subjects probably in order to control the handedness variable [START_REF] Waites | Effect of prior cognitive state on resting state networks measured with functional connectivity[END_REF] . Here we demonstrate that asymmetries in brain lobes due to handedness are not only anatomical but also functional. According to this impact of handedness on BFC in controls, we controlled this variable by comparing individual results in patients to the subgroup of controls with the same handedness. However, the fact that patients might have handedness changes due to pathological plasticity must also be acknowledged.

Basal functional connectivity decreases in MTLE is not specific of EZ

In patients, at an individual level, BFC decreases were often bilateral even if the epileptic side was preferentially affected. Therefore, not surprisingly, we found a low specificity and sensitivity of BFC decreases as a diagnostic test for EZ localization. This is concordant with other imaging studies concerning structural as well as metabolic data. Bilateral structural abnormalities have already been shown using morphometry and diffusion weighted imaging even in patients presenting with visible unilateral HS [START_REF] Sankar | Temporal lobe epilepsy: differential pattern of damage in temporopolar cortex and white matter[END_REF][START_REF] Bernhardt | Mapping limbic network organization in temporal lobe epilepsy using morphometric correlations: insights on the relation between mesiotemporal connectivity and cortical atrophy[END_REF][START_REF] Seidenberg | Ipsilateral and contralateral MRI volumetric abnormalities in chronic unilateral temporal lobe epilepsy and their clinical correlates[END_REF][START_REF] Araujo | Volumetric evidence of bilateral damage in unilateral mesial temporal lobe epilepsy[END_REF][START_REF] Cook | Hippocampal volumetric and morphometric studies in frontal and temporal lobe epilepsy[END_REF][START_REF] Thivard | Productive and perceptive language reorganization in temporal lobe epilepsy[END_REF] . In addition, bilateral metabolic abnormalities were also measured by magnetic resonance spectroscopy and positron emission tomography with fluoro-desoxyglucose in mesial temporal structures [START_REF] Guye | Metabolic and electrophysiological alterations in subtypes of temporal lobe epilepsy: a combined proton magnetic resonance spectroscopic imaging and depth electrodes study[END_REF][START_REF] Didelot | PET imaging of brain 5-HT1A receptors in the preoperative evaluation of temporal lobe epilepsy[END_REF] . Such abnormalities may be observed in the presence or absence of HS and may involve areas devoid of any visible lesions [START_REF] Bernhardt | Mapping limbic network organization in temporal lobe epilepsy using morphometric correlations: insights on the relation between mesiotemporal connectivity and cortical atrophy[END_REF] . The concordance between BFC and these metabolic abnormalities which have been demonstrated to be linked to interictal epileptiform discharges (IED), suggests a potential relationship between BFC and spikes [START_REF] Guye | Metabolic and electrophysiological alterations in subtypes of temporal lobe epilepsy: a combined proton magnetic resonance spectroscopic imaging and depth electrodes study[END_REF][START_REF] Park | Interictal epileptiform discharges relate to 1H-MRSdetected metabolic abnormalities in mesial temporal lobe epilepsy[END_REF] .

The most involved areas in these alterations were the EC and AntHip. These two structures have a crucial role in the epileptogenic network of TLE [START_REF] Bernhardt | Mapping limbic network organization in temporal lobe epilepsy using morphometric correlations: insights on the relation between mesiotemporal connectivity and cortical atrophy[END_REF][START_REF] Bartolomei | Entorhinal cortex involvement in human mesial temporal lobe epilepsy: an electrophysiologic and volumetric study[END_REF][START_REF] Bartolomei | Epileptogenicity of brain structures in human temporal lobe epilepsy: a quantified study from intracerebral EEG[END_REF][START_REF] Bernasconi | Mesial temporal damage in temporal lobe epilepsy: a volumetric MRI study of the hippocampus, amygdala and parahippocampal region[END_REF] . This has been demonstrated by their participation to the dynamics of seizure onset using depth recording, and also by the atrophy that preferentially affects these structures in MTLE patients. EC and hippocampus interactions have been well known for decades, EC being the privileged relay of the afferent pathways to the hippocampus from neocortical areas [START_REF] Insausti | Entorhinal cortex of the rat: cytoarchitectonic subdivisions and the origin and distribution of cortical efferents[END_REF][START_REF] Insausti | The entorhinal cortex of the monkey: II. Cortical afferents[END_REF][START_REF] Witter | Entorhinal cortex of the monkey: V. Projections to the dentate gyrus, hippocampus, and subicular complex[END_REF] . Finally, a quantified study of epileptogenicity based on the measure of fast EEG discharges at seizure onset has recently shown that these two structures are the most epileptogenic in patients with MTLE associated with normal MRI or HS [START_REF] Bartolomei | Epileptogenicity of brain structures in human temporal lobe epilepsy: a quantified study from intracerebral EEG[END_REF] . BFC decreases are concordant with a recent structural connectivity study conducted in a very large group of TLE patients, demonstrating bilateral decreased connectivity between the EC and the other mesial temporal structures as well as the neocortex [START_REF] Bernhardt | Mapping limbic network organization in temporal lobe epilepsy using morphometric correlations: insights on the relation between mesiotemporal connectivity and cortical atrophy[END_REF] . Such functional and structural connectivity alterations are likely to be linked to plasticity secondary to both generation and propagation of repetitive epileptiform discharges [START_REF] Morrell | Varieties of human secondary epileptogenesis[END_REF][START_REF] Sutula | Secondary epileptogenesis, kindling, and intractable epilepsy: a reappraisal from the perspective of neural plasticity[END_REF][START_REF] Wilder | The mirror focus and secondary epileptogenesis[END_REF] .

It is noteworthy that the results of resting state fcMRI studies apparently contradict those based on EEG connectivity. Indeed, an increase in functional connectivity in the EZ has been measured from intracranial recordings [START_REF] Schevon | Cortical abnormalities in epilepsy revealed by local EEG synchrony[END_REF][START_REF] Bettus | Enhanced EEG functional connectivity in mesial temporal lobe epilepsy[END_REF] . The mechanisms underlying these discrepancies are unknown to date and require further investigations. However, one of the main hypotheses remains a neurovascular decoupling in epileptic patients [START_REF] Bettus | Enhanced EEG functional connectivity in mesial temporal lobe epilepsy[END_REF] . Another hypothesis is that EEG connectivity and fcMRI capture different phenomena occurring on different time scale. Indeed, fcMRI is based on the cross-correlations between very slow fluctuations signals (< 0.1 Hz) whereas EEG connectivity considers a broader frequency band but usually above 0.5 Hz (and up to 100-200 Hz) [START_REF] Fox | Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging[END_REF][START_REF] Schevon | Cortical abnormalities in epilepsy revealed by local EEG synchrony[END_REF][START_REF] Bettus | Enhanced EEG functional connectivity in mesial temporal lobe epilepsy[END_REF] . This may suggest complementary information from both modalities rather than a real discrepancy. Thus, in epileptic patients functional connectivity based on slow fluctuations could be rather linked to the actual functional integrity of the macrosystems studied 6, 14 whereas the one based on faster fluctuations could be rather linked to abnormally high connectivity secondary to pathological electrophysiological changes [START_REF] Schevon | Cortical abnormalities in epilepsy revealed by local EEG synchrony[END_REF][START_REF] Bettus | Enhanced EEG functional connectivity in mesial temporal lobe epilepsy[END_REF][START_REF] Wendling | Spatial analysis of intracerebral electroencephalographic signals in the time and frequency domain: identification of epileptogenic networks in partial epilepsy[END_REF] .

Increased basal functional connectivity is specific to the contralateral lobe to EZ

Because BFC increase is almost exclusively localized in the contralateral lobe to EZ, its detection permits indirect localization of the EZ with a very high specificity. Moreover, BFC increases involved more specifically PostHip, AntHip and Amy. Increased functional connectivity of the hippocampus and Amy has already been described in patients with Alzheimer's disease compared to controls during a delayed match-to-sample face recognition task [START_REF] Rosenbaum | Altered connectivity among emotionrelated brain regions during short-term memory in Alzheimer's disease[END_REF] . This increased connectivity was interpreted as reflecting an implicit signalling of emotional content to increase memory capacities. Although our resting state protocol did not allow assessment of connectivity behaviour specifically during this type of task, we hypothesise that BFC increases may reflect the same kind of compensatory mechanisms. In addition it is now accepted that MTL not only plays a crucial role in declarative memory [START_REF] Nadel | Multiple trace theory of human memory: computational, neuroimaging, and neuropsychological results[END_REF][START_REF] Squire | The medial temporal lobe[END_REF] , but is also implicated in WM [START_REF] Spiers | Hippocampal amnesia[END_REF][START_REF] Axmacher | Interaction of working memory and long-term memory in the medial temporal lobe[END_REF][START_REF] Axmacher | Sustained neural activity patterns during working memory in the human medial temporal lobe[END_REF][START_REF] Wagner | Differential effect of side of temporal lobe epilepsy on lateralization of hippocampal, temporolateral, and inferior frontal activation patterns during a verbal episodic memory task[END_REF] . Altered WMQ in patients corroborate these findings. We observed altered WMQ in both left and right MTLE patients, and for both visual and auditory subtests.

Moreover, the significant correlations between WMQ and increased BFC also support the hypothesis of MTL involvement in WM processes. However, our study was focused on an a priori network involving only the MTL structures and did not include extra-temporal regions for statistical considerations (limited number of ROIs for multiple comparisons). It is thus possible that alterations of WMQ were also linked to wider functional network dysfunctions such as those involving frontal lobe. Accordingly, Bernhardt et al. found an increased structural connectivity between EC and homolateral orbitofrontal areas suggesting other compensatory mechanisms involving extra-temporal areas [START_REF] Bernhardt | Mapping limbic network organization in temporal lobe epilepsy using morphometric correlations: insights on the relation between mesiotemporal connectivity and cortical atrophy[END_REF] . In addition WMQ is a sensitive test frequently affected in neurological disorders despite its lack of specificity [START_REF] Bosma | Synchronized brain activity and neurocognitive function in patients with low-grade glioma: a magnetoencephalography study[END_REF] . This could also explain why, in this context, this cognitive test is found to correlate with BFC increases.

Diagnostic power of BFC to localize EZ

This study mainly shows that fcMRI is not able to directly determine the regions involved in seizure generation, since bilateral decrease in temporal lobe structures is the rule. However, BFC increases may be a reliable indicator of functional changes in the region contralateral to the EZ.

Currently, several studies have reported fcMRI data in drug-resistant partial epilepsies [START_REF] Thivard | Productive and perceptive language reorganization in temporal lobe epilepsy[END_REF][START_REF] Weber | Presurgical language fMRI in patients with drugresistant epilepsy: effects of task performance[END_REF][START_REF] Bonelli | Preoperative amygdala fMRI in temporal lobe epilepsy[END_REF] .

However, to our knowledge, none have concentrated on individual results and on the potential clinical usefulness of the technique in the presurgical assessment of these epilepsies. The advantage of our study is that we were able to determine with a specificity of about 91% the EZ lateralization. This method could represent an interesting clinical tool to help physicians.

We acknowledge that the majority of patients presented with HS which is already a robust feature for EZ lateralization. However, contralateral increased BFC was also found in 7 of the 9 patients with no HS. Our findings suggest that such BFC alterations represent a functional plasticity secondary to epileptic processes independent of the causal lesion. This is in accordance with the structural connectivity study by Bernhardt et al. which found no significant relationship between TLE-related atrophy and EC connectivity [START_REF] Bernhardt | Mapping limbic network organization in temporal lobe epilepsy using morphometric correlations: insights on the relation between mesiotemporal connectivity and cortical atrophy[END_REF] . In addition, patients presenting with normal-appearing MRI also exhibited such contralateral increased BFC. Thus, we propose that resting-state fcMRI is a useful technique that could be added to the presurgical assessment of drug-resistant partial epilepsies. All connecting lines correspond to significant basal functional connectivity. Grey lines correspond to links that are not significantly different between right-and left-handed controls.

FIGURES LEGENDS:

Bold lines correspond to links significantly different between right and left handed controls.

Right-handed controls showed significantly higher basal functional connectivity in the left temporal lobe for the links amygdala-posterior hippocampus and anterior-posterior hippocampi (Wilcoxon rank test, corrected p<0.005). Note that for the 2 groups, the link between entorhinal cortex and posterior hippocampus was not significant bilaterally.

Figure 1 :

 1 Figure 1: Differences of basal functional connectivity in controls within mesial temporal

Figure 2 :

 2 Figure 2: Group analysis of altered basal functional connectivity prevalence in

Table 1 .

 1

	Patient	sex age	Handed-ness	EZ laterality	Epilepsy onset (year)	Seizure frequency (/month)	MRI visible lesion	Seizure outcome (Engel's class)
	1	M 26	Left	Left	13	4	No visible MRI lesion	awaiting
	2	M 39	Right	Left	33	6	Left hippocampal sclerosis	I
	3	F 35	Right	Left	16	32	Left hippocampal sclerosis	I
	4	F 23	Right	Left	13	6	Left hippocampal sclerosis	I
	5	M 40	Right	Left	26	8	Left hippocampal sclerosis	I
	6	F 48	Right	Left	14	8	Left hippocampal sclerosis	refused
	7	F 25	Right	Left	16	18	Left hippocampal sclerosis	I
	8	F 41	Right	Left	27	6	Left amygdala hypersignal	I
	9	F 25	Right	Left	18	10	Left parahippocampal focal cortical dysplasia	III
	10	F 61	Right	Left	1	14	Left hippocampal sclerosis	awaiting
	11	M 37	Right	Left	13	10	Left hippocampal dysplasia	I
	12	M 20	Right	Left	7.5	4	Left temporal pole dysplasia	II
	13	M 49	Left	Right	18	18	Right temporal lobe dysplasia	refused
	14	M 57	Left	Right	33	11	Right post-traumatic temporal lobe lesion	refused
	15	M 34	Left	Right	30	12	Right hippocampal sclerosis	I
	16	M 35	Left	Right	15	1	No visible MRI lesion	awaiting
	17	F 41	Left	Right	12	2	Right hippocampal sclerosis	I
	18	F 24	Right	Right	17	2	Right hippocampal sclerosis	awaiting
	19	M 39	Right	Right	8	24	Right MTL cortical dysplasia	awaiting
	20	F 54	Right	Right	13	16	Right hippocampal sclerosis	I
	21	F 57	Right	Right	16	1.5	Right hippocampal sclerosis	I
	22	F 38	Right	Right	12.5	12	Right hippocampal sclerosis	I

Table 1 : Clinical characteristics of MTLE patients

 1

	rare

: For patients who had surgical resection, seizure outcome is classified according to Engel's classification

[START_REF] Engel | Outcome with respect to epileptic seizures[END_REF]

(I: Seizure free, II:

Table 2 : Neuropsychological characteristics of MTLE patients

 2

	Twenty one out of the 22 patients (10 with right

Assessment of WMSIII showed that immediate memory (IMQ) and delayed memory quotients (DMQ) were not significantly altered (respectively p=0.861 and p=0.805) and the same result was observed for the 2 subtests (auditory IMQ p=0.206; visual IMQ p=0.352; auditory DMQ p=0.494; visual DMQ p=0.424). In contrast, the working memory quotient was significantly below normative values (p<0.001), as well as the auditory (p=0.035) and visual (p<0.0001) working memory subtests. Variables as EZ laterality and handedness did not interfere with all quotients (WAIS III and WMS III, Wilcoxon Sign-Rank test). In addition, we did not found any significant discrepancies between visual and auditory subtests in patients (p=0.765 in right MTLE, and p=0.680 in left MTLE using a paired test (Wilcoxon test)). : Evaluation of intelligence scale and memory capacities by the Wechsler adult intelligent scale -Third Edition (WAIS III) (reported scores: full scale intelligence quotient (FSIQ), verbal intelligence quotient (VIQ) and performance intelligence quotient (PIQ)) and the Wechsler memory scale -Third Edition (WMS III) (reported scores: immediate memory quotient (IMQ), delayed memory quotient (DMQ) and working memory quotient (WMQ), all with visual and auditory quotients). Scores in bold correspond to abnormal performances (under 1.5 SD; normal mean = 100).

Table 3

 3

	: Normalized correlation coefficients (and standard deviation) between ROIs in each
	lobe, for right-handed and left-handed controls. Significant left-right lateralization within one
	group of control (willcoxon rank test) *p < 0.05; **p<0.01; ***p<0.001. Significant left handed
	and right handed controls differences (Mann Whitney U-test) ♣ p<0.05, ♣♣ p<0.01, ♣♣♣ p<0.001.

ns: no significant link.

Table 4 : Bilateral regional occurrence of abnormal pair-wise basal functional connectivity in MTLE patients:

 4

	Two out of the 22 MTLE patients showed a normal BFC profile (patients 5 and 11). BFC
	decreases were observed in 18 out of the 22 patients (patients 1-4, 6-8, 10, 12-18, 20-22). Sixteen
	had at least one link BFC decrease in the EZ (patients 1-4, 6, 10, 12-18, 20-22) while 10 had a
	contralateral BFC decrease (patients 1-3, 7, 8, 10, 15, 20-22). Eight had bilateral BFC decreases

Columns correspond to decreased and increased links in patients, relative to controls (above or below 2 standard deviations) in the epileptogenic zone (EZ) and in the contralateral lobe and for each pair of region of interest. Used abbreviations: BFC: Basal Functional Connectivity; EC: Entorhinal Cortex; Amy: Amygdala; TP: Temporal Pole (BA38); AH: Anterior Hippocampus; PH: Posterior Hippocampus.

Table 5 : Sensitivity and specificity of basal functional connectivity (BFC) alterations to lateralize the epileptogenic zone

 5 : a) Diagnostic power of BFC decreases detection to lateralize the epileptogenic zone (EZ). True positive: patients with at least one decreased link in the EZ lobe. False negative: patients with no decreased link in the EZ lobe. False positive: patients with at least one decreased link in the contralateral lobe. True negative: patients with no decreased link in the contralateral lobe. PPV: Positive Predictive Value; NPV: Negative Predictive Value. b) Diagnostic power of the presence of at least one abnormal link showing increased basal functional connectivity to localize the temporal lobe contralateral to EZ. True positive: patients with at least one increased link in the contralateral lobe to EZ. False negative: patients with no increased link in the contralateral lobe to EZ. False positive: patients with at least one increased link in the EZ lobe. True negative: patients with no increased link in the EZ lobe. PPV: Positive Predictive Value; NPV: Negative Predictive Value.

Acknowledgement:

The authors thank Virginie Laguitton for the neuropsychological assessment of patients, and Dr. Aileen McGonigal for the revision of the English version.

Fundings: This work was supported by CNRS, INSERM and ANR (CONNECTEPI). GB is the recipient of a PhD research grant delivered by the Region 'Provence Alpes-Côte d'Azur' and Deltamed.

Competing interests: None

Ethics approval: This study approved by the local Ethics Committee of Marseille Public

Hospital. There is no identifiable patient information in the manuscript.

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