scholarly journals Uncinate Fasciculus in Temporal Lobe Epilepsy

2019 ◽  
Vol 2 (2) ◽  
pp. 01-04
Author(s):  
Simbrón Ribbeck Lourdes ◽  
Sandoval Paredes Josefina ◽  
Amador Sánchez Karen ◽  
Taboada Barajas Jesús

Background and purpose: Temporal lobe epilepsy is the most common focal intractable epilepsy. Uncinate fasciculus is a white fiber bundle that connects the orbitofrontal cortex with the anterior temporal lobe, and is implicated in most of the superior mental functions. There is evidence of uncinate fasciculus as a propagation pathway of seizures from temporal lobe. The aim of the study is to determine uncinate fasciculus alterations in patients with temporal lobe epilepsy, through fractional anisotropy. Methods: Thirty-three patients with temporal lobe epilepsy (10 right and 23 left) were studied. All of them were right-handed and had left hemisphere dominance for language. A 1.5 T MR imaging scanner was used to obtain diffusion tensor imaging (DTI). Fractional anisotropy of uncinate fasciculus was calculated through TBSS (Tract Based Spatial Statistics). Statistical analysis was done using IBM SPSS (v. 25). Results: Fractional anisotropy was higher in right uncinate fasciculus, regardless of epilepsy side. Right uncinate fasciculus, at the insula level, showed lower fractional anisotropy in patients with right temporal lobe epilepsy. Conclusions: Results support the evidence of uncinate fasciculus as a pathway of propagation in temporal lobe epilepsy, specially at insular level.

Epilepsia ◽  
2008 ◽  
Vol 49 (8) ◽  
pp. 1409-1418 ◽  
Author(s):  
Beate Diehl ◽  
Robyn M. Busch ◽  
John S. Duncan ◽  
Zhe Piao ◽  
Jean Tkach ◽  
...  

2021 ◽  
Author(s):  
Nicolò Rolandi ◽  
Fulvia Palesi ◽  
Francesco Padelli ◽  
Isabella Giachetti ◽  
Domenico Aquino ◽  
...  

Temporal lobe epilepsy (TLE) is the most common form of focal epilepsy. Parameters of microstructural abnormalities derived from diffusion tensor imaging(DTI) have been reported to be helpful in differentiating between Left and Right TLE (L-TLE and R-TLE) but few of them compared L-TLE and R-TLE with a voxelwise approach. In this study, a whole brain tract based spatial statistical analysis was performed on DTI, diffusion kurtosis and NODDI derived parameters of 88 subjects to identify specific white matter patterns of alteration in patient affected by L-TLE and R-TLE with respect to healthy controls. Our findings demonstrated the presence of specific patterns of white matter alterations, with L-TLE more widely affected both in cerebral and cerebellar regions. This result supports the need to consider patients separately, according to the side of their pathology.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Bastian David ◽  
Jasmine Eberle ◽  
Daniel Delev ◽  
Jennifer Gaubatz ◽  
Conrad C. Prillwitz ◽  
...  

AbstractSelective amygdalohippocampectomy is an effective treatment for patients with therapy-refractory temporal lobe epilepsy but may cause visual field defect (VFD). Here, we aimed to describe tissue-specific pre- and postoperative imaging correlates of the VFD severity using whole-brain analyses from voxel- to network-level. Twenty-eight patients with temporal lobe epilepsy underwent pre- and postoperative MRI (T1-MPRAGE and Diffusion Tensor Imaging) as well as kinetic perimetry according to Goldmann standard. We probed for whole-brain gray matter (GM) and white matter (WM) correlates of VFD using voxel-based morphometry and tract-based spatial statistics, respectively. We furthermore reconstructed individual structural connectomes and conducted local and global network analyses. Two clusters in the bihemispheric middle temporal gyri indicated a postsurgical GM volume decrease with increasing VFD severity (FWE-corrected p < 0.05). A single WM cluster showed a fractional anisotropy decrease with increasing severity of VFD in the ipsilesional optic radiation (FWE-corrected p < 0.05). Furthermore, patients with (vs. without) VFD showed a higher number of postoperative local connectivity changes. Neither in the GM, WM, nor in network metrics we found preoperative correlates of VFD severity. Still, in an explorative analysis, an artificial neural network meta-classifier could predict the occurrence of VFD based on presurgical connectomes above chance level.


Sign in / Sign up

Export Citation Format

Share Document