Differential pattern of white matter network abnormalities across cognitive phenotypes in temporal lobe epilepsy
Abstract number :
1.372
Submission category :
11. Behavior/Neuropsychology/Language / 11A. Adult
Year :
2018
Submission ID :
493765
Source :
www.aesnet.org
Presentation date :
12/1/2018 6:00:00 PM
Published date :
Nov 5, 2018, 18:00 PM
Authors :
Anny Reyes, University of California - San Diego; Anisa Marshall, University of California - San Diego; Akshara Balachandra, University of California - San Diego; Manu Hegde, University of California - San Francisco; Brianna M Paul, University of Californ
Rationale: Cognitive dysfunction is a common and debilitating comorbidity in patients with temporal lobe epilepsy (TLE). However, there is considerable variability in the cognitive impairments observed across patients, with some patients demonstrating generalized impairment and others demonstrating relatively normal cognitive profiles. Given that TLE is now understood to represent a spectrum of disorders, characterizing cognitive phenotypes may help in identifying network disruptions associated with distinct TLE syndromes. In this study, we sought to identify cognitive phenotypes based on neuropsychological measures and evaluate patterns of white matter disruption and clinical features associated with each phenotype. Methods: Sixty-four patients with TLE were grouped into four distinct cognitive phenotypes based on impairment on language and verbal memory measures (Language and Memory Impaired = 16, Memory Impaired only 13; Language Impaired only= 18; No Impairment =17). Diffusion tensor imaging was obtained in all patients and in 46 healthy controls (HC). The integrity of the white matter directly beneath the neocortex (i.e., superficial white matter; SWM) and the integrity of white matter (WM) tracts associated with memory and language processing were evaluated across the four phenotypes. Fractional anisotropy (FA) and mean diffusivity (MD) were calculated for the arcuate (ARC), uncinate fasciculus (UNC), fornix (FX), parahippocampal cingulum (PHC), and inferior longitudinal fasciculus (ILF). For the SWM, FA and MD were calculated by sampling1 mm below the pial surface at each vertex across the cortical surface. Results: MD of the SWM showed a robust pattern of differences across phenotypes, whereas group differences in FA were more prominent in deep WM association tracts (see Figure). Analysis of surface maps revealed that the Language and Memory impaired group demonstrated left lateralized increases in MD that were pronounced within lateral temporal SWM, whereas the Memory Impaired group demonstrated widespread increases in MD that included medial frontal and temporal lobe (i.e., parahippocampal and entorhinal) SWM, bilaterally. Relative to HC, the Language and Memory group showed decreased FA in ARC and ILF, bilaterally, and left UNC and increases in MD in left ILF and UNC (all p-values p< 0.05). The Memory Impaired group showed a trend for higher MD of left ILF relative to HC (p= 0.057). There were no significant microstructural abnormalities observed in the Language Impaired group or the No Impairment group relative to HC. There were no differences in side of seizure onset, MTS status, or disease duration across groups. However, the Memory Impaired group had an older age of seizure onset compared to all other patient groups (p= 0.001). Conclusions: These findings reveal that cognitive phenotypes in TLE have unique WM network signatures that are not well explained by important clinical features. These differences are observed in both deep WM association tracts and the SWM, and appear particularly pronounced for SWM MD. The unique microstructural changes observed in each phenotype help to unravel the neurobiology associated with cognitive impairment in TLE, and could be used in combination with clinical data to help predict risk for cognitive decline associated with aging or medical/surgical interventions in TLE. Funding: This work was supported by the National Institute of Health (R01NS065838 to C.R.M.).