3 research outputs found
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The white matter connectome as an individualized biomarker of language impairment in temporal lobe epilepsy.
ObjectiveThe distributed white matter network underlying language leads to difficulties in extracting clinically meaningful summaries of neural alterations leading to language impairment. Here we determine the predictive ability of the structural connectome (SC), compared with global measures of white matter tract microstructure and clinical data, to discriminate language impaired patients with temporal lobe epilepsy (TLE) from TLE patients without language impairment.MethodsT1- and diffusion-MRI, clinical variables (CVs), and neuropsychological measures of naming and verbal fluency were available for 82 TLE patients. Prediction of language impairment was performed using a robust tree-based classifier (XGBoost) for three models: (1) a CV-model which included demographic and epilepsy-related clinical features, (2) an atlas-based tract-model, including four frontotemporal white matter association tracts implicated in language (i.e., the bilateral arcuate fasciculus, inferior frontal occipital fasciculus, inferior longitudinal fasciculus, and uncinate fasciculus), and (3) a SC-model based on diffusion MRI. For the association tracts, mean fractional anisotropy was calculated as a measure of white matter microstructure for each tract using a diffusion tensor atlas (i.e., AtlasTrack). The SC-model used measurement of cortical-cortical connections arising from a temporal lobe subnetwork derived using probabilistic tractography. Dimensionality reduction of the SC was performed with principal components analysis (PCA). Each model was trained on 49 patients from one epilepsy center and tested on 33 patients from a different center (i.e., an independent dataset). Randomization was performed to test the stability of the results.ResultsThe SC-model yielded a greater area under the curve (AUC; .73) and accuracy (79%) compared to both the tract-model (AUC: .54, p < .001; accuracy: 70%, p < .001) and the CV-model (AUC: .59, p < .001; accuracy: 64%, p < .001). Within the SC-model, lateral temporal connections had the highest importance to model performance, including connections similar to language association tracts such as links between the superior temporal gyrus to pars opercularis. However, in addition to these connections many additional connections that were widely distributed, bilateral and interhemispheric in nature were identified as contributing to SC-model performance.ConclusionThe SC revealed a white matter network contributing to language impairment that was widely distributed, bilateral, and lateral temporal in nature. The distributed network underlying language may be why the SC-model has an advantage in identifying sub-components of the complex fiber networks most relevant for aspects of language performance
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Impaired Behavioral Pattern Separation in Refractory Temporal Lobe Epilepsy and Mild Cognitive Impairment.
ObjectiveEpisodic memory impairment and hippocampal pathology are hallmark features of both temporal lobe epilepsy (TLE) and amnestic mild cognitive impairment (aMCI). Pattern separation (PS), which enables the distinction between similar but unique experiences, is thought to contribute to successful encoding and retrieval of episodic memories. Impaired PS has been proposed as a potential mechanism underling episodic memory impairment in aMCI, but this association is less established in TLE. In this study, we examined behavioral PS in patients with TLE and explored whether profiles of performance in TLE are similar to aMCI.MethodPatients with TLE, aMCI, and age-matched, healthy controls (HCs) completed a modified recognition task that relies on PS for the discrimination of highly similar lure items, the Mnemonic Similarity Task (MST). Group differences were evaluated and relationships between clinical characteristics, California Verbal Learning Test-Second Edition scores, and MST performance were tested in the TLE group.ResultsPatients with TLE and aMCI demonstrated poorer PS performance relative to the HCs, but performance did not differ between the two patient groups. Neither the side of seizure focus nor having hippocampal sclerosis affected performance in TLE. However, TLE patients with clinically defined memory impairment showed the poorest performance.ConclusionMemory performance on a task that relies on PS was disrupted to a similar extent in TLE and aMCI. The MST could provide a clinically useful tool for measuring hippocampus-dependent memory impairments in TLE and other neurological disorders associated with hippocampal damage