Abstracts

Hippocampal-Language Network Laterality in Temporal Lobe Epilepsy: A Novel Measure Using Resting-State Functional Connectivity

Abstract number : 110
Submission category : 5. Neuro Imaging / 5B. Functional Imaging
Year : 2020
Submission ID : 2422458
Source : www.aesnet.org
Presentation date : 12/5/2020 9:07:12 AM
Published date : Nov 21, 2020, 02:24 AM

Authors :
Allison Whitten, Vanderbilt University Institute of Imaging Sciences; Monica Jacobs - Vanderbilt University Medical Center; Dario Englot - Vanderbilt University Institute of Imaging Sciences, Vanderbilt University; Kaela Levine - Vanderbilt University Ins


Rationale:
Determining language lateralization is a crucial part of the epilepsy presurgical evaluation to inform the potential for language deficits, especially in patients with temporal lobe epilepsy. The most common method for measuring language lateralization uses fMRI language tasks. However, this measure is dependent upon the specific language task used (which varies widely across epilepsy centers), as well as the individual performance and compliance of the patient during the task. In this study, we explored a novel resting-state functional connectivity measure of language lateralization in temporal lobe epilepsy patients by assessing laterality of the hippocampal-language network.
Method:
Presurgical resting-state 3T fMRI data was acquired from 54 healthy controls, 27 right TLE patients (RTLE), and 13 left TLE patients (LTLE). The bilateral hippocampi were segmented into anterior and posterior sections using FreeSurfer, and 11 frontal and temporo-parietal language regions were segmented using MultiAtlas [1]. Resting-state language lateralization (LI-Rest) was calculated using the Quantitative Intrinsic Laterality Index [2], where the seed was always the anterior or posterior hippocampus and the target was the frontal or temporo-parietal language regions. This resulted in four LI-Rest measures per subject.
Results:
The LI-Rest measures were found to produce lateralized values in the majority of subjects (68.1%), defined as values ≥ 0.20 for left lateralization and ≤ -0.20 for right lateralization. Repeated measures ANOVA revealed no significant differences in LI strength between Hippocampus Region (anterior, posterior), Language Region (frontal, temporo-parietal), or Group (control, RTLE, LTLE). However, there was a significant Language Region x Group interaction (p = .01), in which the LTLE group showed significantly more right lateralization than RTLE in the temporo-parietal language regions (LTLE M= -0.24 ± 0.43, RTLE M= 0.06 ± 0.52; p = .02; see Figure 1). In addition, within the LTLE group only, significant negative correlations were found between two LI-Rest measures and the CVLT Long Delay test of verbal memory (see Figure 2; both p < .05).
Conclusion:
Our results suggest that a language lateralization measure acquired from resting-state functional connectivity between the hippocampus and language regions may be a useful tool to detect lateralization in cases where task LI is not suitable, and warrants further investigation in a larger sample. In particular, our LI-Rest measures detected greater right lateralization in temporo-parietal regions closest to seizure foci in LTLE patients – a compensatory mechanism of reorganization that has also been found in fMRI task lateralization studies. Furthermore, stronger right sided LI to frontal and temporo-parietal regions were associated with better neurocognitive scores in the same group. Investigations are ongoing to assess whether the LI-Rest measures are comparable to LI from fMRI language tasks, and if they may be used to predict postsurgical language and verbal memory outcomes. 1. Asman, A. J., & Landman, B. A. (2013). Non-local statistical label fusion for multi-atlas segmentation. Medical image analysis, 17(2), 194-208. 2. Liu, H., Stufflebeam, S. M., Sepulcre, J., Hedden, T., & Buckner, R. L. (2009). Evidence from intrinsic activity that asymmetry of the human brain is controlled by multiple factors. Proceedings of the National Academy of Sciences, 106(48), 20499-20503.
Funding:
:NIH T32 EB001628 NIH R01 NS075270 NIH R01 NS110130 NIH R01 NS108445 NIH R00 NS097618
Neuro Imaging