Impaired naming performance in temporal lobe epilepsy: language fMRI responses are modulated by disease characteristics

Abstract

Objective To investigate alterations of language networks and their relation to impaired naming performance in temporal lobe epilepsy (TLE) using functional MRI. Methods Seventy-two adult TLE patients (41 left) and 36 controls were studied with overt auditory and picture naming fMRI tasks to assess temporal lobe language areas, and a covert verbal fluency task to probe frontal lobe language regions. Correlation of fMRI activation with clinical naming scores, and alteration of language network patterns in relation to epilepsy duration, age at onset and seizure frequency, were investigated with whole-brain multiple regression analyses. Results Auditory and picture naming fMRI activated the left posterior temporal lobe, and stronger activation correlated with better clinical naming scores. Verbal fluency MRI mainly activated frontal lobe regions. In left and right TLE, a later age of epilepsy onset related to stronger temporal lobe activations, while earlier age of onset was associated with impaired deactivation of extratemporal regions. In left TLE patients, longer disease duration and higher seizure frequency were associated with reduced deactivation. Frontal lobe language networks were unaffected by disease characteristics. Conclusions While frontal lobe language regions appear spared, temporal lobe language areas are susceptible to dysfunction and reorganisation, particularly in left TLE. Early onset and long duration of epilepsy, and high seizure frequency, were associated with compromised activation and deactivation patterns of task-associated regions, which might account for impaired naming performance in individuals with TLE. Electronic supplementary material The online version of this article (10.1007/s00415-020-10116-x) contains supplementary material, which is available to authorized users.

Decoupling of functional and structural language networks in temporal lobe epilepsy

“…The relation of PPI activation to out‐of‐scanner naming performance (GNT 15 scores) was explored using multiple regression. In accord with previous investigations, 2 , 3 correlational activations are shown at a threshold of p < .001 uncorrected.…”

“…Posterobasal temporal regions are strongly involved in clinical naming performance, 3,7 which highlights the importance of using fMRI tasks that reliably activate these brain regions in the evaluation of TLE. 2,6 We used our previously validated overt auditory and picture naming fMRI tasks with active control conditions. Monitoring task execution demonstrated excellent task performance in all subjects, supporting the interpretation that our findings relate to disease-specific network alterations rather than intergroup differences in task performance.…”

“…Impairment of language function, in particular naming and word‐finding difficulties, is a frequent concern in patients with temporal lobe epilepsy (TLE). 1 Advanced neuroimaging techniques, such as functional magnetic resonance imaging (fMRI) 2 , 3 and diffusion MRI (dMRI), 4 , 5 are increasingly applied to study the complex organization of functional and structural language networks.…”

Combining Gamma With Alpha and Beta Power Modulation for Enhanced Cortical Mapping in Patients With Focal Epilepsy

“…However, with the cross-validation procedure implemented in our analysis it is possible to include a multitude of indicators. Other factors that might further improve mapping could include power, adding modulations from other frequency bands such as theta or delta (Daume et al, 2017;Grooms et al, 2017), power modulations recorded during additional tests, and responses measured in other modalities (e.g., functional mapping using fMRI; Picht et al, 2013;Trimmel et al, 2019Trimmel et al, , 2020. Prior predictions about the likelihood of eloquence at a particular cortical area may also be of interest and help to increase mapping accuracy.…”

Characterizing the hyper‐ and hypometabolism in temporal lobe epilepsy using multivariate machine learning

“…To verify whether all the abnormal metabolic patterns are driven by this clinical factor, we examined the association between different metabolic patterns and age at epilepsy onset using generalized linear models. As age at epilepsy onset negatively correlated with disease duration (Pearson's r = −0.480, p = 0.020), we used disease duration as a covariate while testing on age at epilepsy onset during the association analyses (Trimmel et al., 2020).…”