Manifold learning reveals anomalies of language and memory processing in patients with temporal lobe epilepsy

International audience ; Introduction: Owing to recurrent seizures, temporal lobe epilepsy (TLE) patients show neural reorganization, which has especially been studied in language and memory networks (Baciu & Perrone-Bertolotti, 2015; Sidhu et al., 2013). Although these two functions have mainly been studied separately, recent evidence indicates their dynamic interaction (Duff & Brown-Schmidt, 2017). Driven by this neural coupling, we are interested in exploring abnormal patterns of activation within the joint language and episodic memory network in TLE patients over the tasks that engage one or the other function or both simultaneously. However, the problem of exploring complex structures is related to the high dimensionality of data and joint variation between the regions, which, in addition, to sample size, restrict the usage of standard statistical analysis. We will present an application of non-linear manifold learning for fMRI data dimension reduction as one way of addressing these issues. Methods: The GE2REC fMRI protocol (Banjac et al., 2019) was used with 21 healthy adults and 12 left TLE patients to obtain language data through sentence generation (GE), episodic memory data through visual recognition (RECO) and mixed language and memory data through recalling and generating sentences (RA). The ROIs were defined as the regions of Brainnetome atlas (Fan et al., 2016) in which more than 40% of the voxels were activated (p <.001, uncorrected; k > 20) in the control group during the tasks. The beta weights were extracted using the Rex toolbox. In order to explore the deviation of language and memory network activation patterns in TLE patients, the following steps were performed (Tilquin et al, 2019): 1. Dimensionality reduction by using the Umap (McInnes, Healy & Melville, 2018) to convert a set of standardized ROI beta values into a unique point in a manifold subspace representing one participant during one task. 2. Model estimation based on the formula Y=f(x)+ε. Where Y is the control ...