The Effect of Area Deprivation Index on White Matter Connectome in Temporal Lobe Epilepsy
Abstract number :
2.456
Submission category :
5. Neuro Imaging / 5A. Structural Imaging
Year :
2022
Submission ID :
2233012
Source :
www.aesnet.org
Presentation date :
12/4/2022 12:00:00 PM
Published date :
Nov 22, 2022, 05:29 AM
Authors :
Daniel Chu, BA – University of Wisconsin School of Medicine and Public Health; Nagesh Adluru, PhD – University of Wisconsin Madison; Veena Nair, PhD – University of Wisconsin Madison; Timothy Choi, MD – University of Wisconsin Madison; Anusha Adluru, MTech – University of Wisconsin Madison; Kevin Dabbs, MS – University of Wisconsin Madison; Jedidiah Mathis, BS – Medical College of Wisconsin; Andrew Nencka, PhD – University of Wisconsin Madison; Carson Gundlach, BS – University of Wisconsin Madison; Andrew Alexander, PhD – University of Wisconsin Madison; Lisa Conant, PhD – Medical College of Wisconsin; Jeffrey Binder, MD – Medical College of Wisconsin; Mary Meyerand, PhD – University of Wisconsin Madison; Aaron Struck, MD – University of Wisconsin Madison; Bruce Hermann, PhD – University of Wisconsin Madison; Vivek Prabhakaran, MD PhD – University of Wisconsin Madison
This is a Late Breaking abstract
Rationale: Social determinants of health, including the effects of neighborhood disadvantage and socioeconomic status, have been implicated as having a significant impact on epilepsy prevalence and outcomes. Our study aim is to characterize the degree of aberrant white matter connectivity in temporal lobe epilepsy (TLE) patients compared to healthy controls as a function of disadvantage using a U.S. census-based neighborhood disadvantage metric, the area deprivation index (ADI).
Methods: We employed multi-shell connectome diffusion weighted MRI (ms-dMRI) measurements from 119 participants, ages 18 to 60, from the Epilepsy Connectome Project (ECP) including 74 patients with TLE (29 male, mean age = 39.28 ± 11.71 years) and 45 healthy controls (HCs) (23 male, mean age = 31.87 ± 10.14 years). The ms-dMRI data were pre-processed using the DESIGNER guidelines. The IIT Destrieux gray matter atlas was used to derive the 162 x 162 structural connectivity matrices (SCMs) using MRTrix3. ComBat data harmonization was applied to harmonize the SCMs from scanner upgrade acquisitions. Threshold free network-based statistics (TFNBS) was used for statistical analysis of the harmonized SCMs. These findings were correlated with the ADI which reflects 17 area-level indicators of poverty, employment, education, and physical conditions. Participants were categorized into two ADI groups representing low disadvantage (quintiles 1 and 2) and high disadvantage (quintiles 4 and 5).
Results: Figure 1 illustrates the relationship between ADI and DWI connectome abnormalities. More disadvantaged TLE demonstrate enhanced significant differences when compared to the more disadvantaged HCs. When comparing the more disadvantaged TLE group to the less disadvantaged TLE group, the differences appear to be modest and trend level. Moreover, less disadvantaged TLE patients also exhibit a significantly greater number of abnormal connections reflected in lower expected CSA compared to lower disadvantage HCs—inferring a general impact of epilepsy. A representative white matter connectome CSA association comparing more disadvantaged to less disadvantaged TLE patients is depicted in Figure 2.
Conclusions: Our results demonstrates that (1) disadvantage as indexed by the ADI is related to white matter structure and integrity in TLE, and (2) TLE exerts a general impact on DWI connectome status as both low and high disadvantage TLEs demonstrate more abnormality compared to low and high disadvantage controls respectively. The former expands understanding of deprivation effects in TLE, and in particular the relationship of a geospatial metric of neighborhood disadvantage (ADI) with metrics of white matter integrity. Further investigation of deprivation effects on white matter integrity should advance understanding of the network abnormalities that underlie cognitive and behavioral comorbidities of epilepsy.
Funding: We are grateful for the support from the AES Pre-doctoral Fellowship, T32 GM140935, UW MSTP Radiology Fellowship, R01NS123378, R01NS105646, R01NS105646, R01NS111022, P50HD105353.
Neuro Imaging