Temporal lobe epilepsy (TLE) accounts for 70-80% of epilepsy in adults (1). The dysfunction (epileptic zone, EZ) is located in the temporal lobe and induces language and memory reorganization of cerebral networks and various degrees of cognitive efficiency. TLE patients should be explored according to a multimodal integrative perspective, based on multiple data on brain activation, cerebral structure and cognitive performance. We present two patients with left TLE compared to a control group (N=12). Data acquisition. Patients underwent neuropsychological, fMRI and MR-DTI. Control group underwent fMRI and MR-DTI. Neuropsychological scores for patients were compared to normative matched samples associated with each of the tests used. Functional MRI and MR-DTI experiments were performed in a whole-body 3T MR scanner (Philips Achieva). Parameters of diffusion tensor sequence for MR-DTI acquisition were: voxel size: 2 x 2 x 2 mm, 128 x 126 slices of 2 mm thickness and no gap, TE = 67.2 ms, TR = 14000ms, EPI factor = 63, field of view = 256 mm, b value = 1500sec / mm2. During fMRI we applied the NEREC protocol (2) to map lexical production. Parameters for manufacturer-provided gradient-echo/T2* weighted EPI used for fMRI were: voxel size 2.75 x 2.75 x 3.5 mm, field of view 220 x 220 x 131.75 acquired with a 88 x 85 pixels and a reconstruction matrix of 96 x 96; TR=2.5 sec, TE=30 msec and flip angle=77°. Data processing. DICOM raw data for MR-DTI was converted to NIFTI (dcm2niix toolbox) before concatenation and pre-processing with Diffusionist software (2,3) running on Linux and based on FSL (FMRIB Software Library v5.0, Oxford UK (4,5) and FMRIB Diffusion Toolbox (FDT). We measured Fractional Anisotropy (FA) to assess white matter (WM) modifications at (a) intra-hemispheric: inferior longitudinal (ILF), inferior fronto-occipital (IFOF), superior longitudinal SLF, arcuate (Arc), uncinate (Unc), cingulum (Cing), fornix (Fox), and (b) inter-hemispheric level in terms of sub-regions of the corpus callosum (CC): genu (GCC), body (BCC) and splenium (SCC). SPM (www.fil.ion.ucl.ac.uk/spm) implemented in MATLAB (Mathworks Inc., Natick, MA, USA) was used to analyse fMRI data. After pre-processing, statistical analyses were performed individually and then at a group level for controls (one-sample t-test; k > 5; p<0.05 FWE corrected; t = 8.5). Based on fMRI activation of frontal region (left, right) we calculated frontal lateralization indices (LI), via the LI-tool implemented in the SPM12 toolbox (6) for each patient and for the control group. Patient 1. P1 showed normal performances for verbal and memory but decline of executive functioning. At p ≤ .05, FA was significantly decreased compared to controls for several WM bundles at the left such as ILF (t'(12) = -2.169, p = .03), Unc (t'(12) = -1.778, p = .05), and Fox (t'(12) = -1.801, p = .05), and the right Unc (t'(12) = -1.922, p = .04). No differences were observed for the corpus callosum (t'(12) = -0.18, p = .4). At p <.05 corrected (t ≥ 8.5) and k > 5, frontal LI was -0.412, suggesting right hemisphere specialization of P1 compared to controls (left-hemisphere lateralized). Patient 2. P2 showed normal results for memory tests but was impaired for phonological (phonological fluency= -1,86 σ) and lexico-semantic (object naming= -1,65 σ) processes. Lower FA values (p ≤ .05) were obtained for left Unc (t'(12) = -1.671, p = .05) and right Arc (t'(12) = -3.431, p < .05). P2 also showed lower FA of BCC (t '(12) = -1.922, p <.05). At p <.05 corrected (t ≥ 8.5) and k > 5, frontal IL was 0.165, suggesting bilateral representation of language. The integration of multimodal data is a current issue. While different information derived from MRI neuroimaging and cognitive scores is interrelated, it appears important to go beyond the descriptive approach and move towards a statistical integration of these different data. Indeed, the multimodal integration of anatomical, functional and cognitive data make possible to draw-up comprehensive neurocognitive patterns in patients with epilepsy. In this way, efficient versus less efficient reorganization profiles in this pathology will be identified. This will lead to fundamental and clinical advances such as the identification of functional areas, the prediction of post-surgical outcomes in case of curative neurosurgery, the anticipation/optimization of the rehabilitation if necessary and all this with a view to moving towards an individualized and customized medicine.