Behavioral Phenotypes in Temporal Lobe Epilepsy and Their Clinical and Network Features
Abstract number :
2.303
Submission category :
11. Behavior/Neuropsychology/Language / 11A. Adult
Year :
2022
Submission ID :
2204651
Source :
www.aesnet.org
Presentation date :
12/4/2022 12:00:00 PM
Published date :
Nov 22, 2022, 05:26 AM
Authors :
Aaron Struck, MD – University of Wisconsin School of Medicine and Public Health; Jeffery Binder, MD – Medical College of Wisconsin; Lisa Conant, PhD – Medical College of Wisconsin; Kevin Dabbs, MS – University of Wisconsin; Camille Garcia-Ramos, PhD – University of Wisconsin; Bruce Hermann, PhD – University of Wisconsin; Marybeth Meyerand, PhD – University of Wisconsin; Veena Nair, PhD – University of Wisconsin; Vivek Prabhakaran, MD, PhD – University of Wisconsin
This abstract has been invited to present during the Genetics & Behavior/Neuropsychology/Language platform session.
Rationale: To identify phenotypes of self-reported symptoms of psychopathology and their clinical and network characteristics in patients with temporal lobe epilepsy (TLE).
Methods: A total of 114 patients with TLE and 83 controls from the Epilepsy Connectome Project were administered the Achenbach Adult Self-Report (ASR) inventory from which the six DSM-oriented scales were analyzed by unsupervised machine learning analytics to identify latent TLE groups. Identified clusters were contrasted to controls to characterize their association with sociodemographic, clinical epilepsy, and morphological and functional imaging network features.
Results: TLE patients overall exhibited significantly higher (abnormal) scores across all ASR DSM-oriented scales compared to controls. However, cluster analysis identified three latent groups: 1) unimpaired with no scale elevations compared to controls (Cluster 1, 37% of TLE patients), 2) mild to moderate symptomatology characterized by significant elevations across several ASR syndrome scales compared to controls (Cluster 2, 42% of TLE patients), and 3) marked symptomatology with significant elevations across all scales compared to controls and the other TLE behavior phenotype groups (Cluster 3, 21% of TLE patients) (Figure 1). Concurrent validity of the behavioral phenotype grouping was demonstrated through their stepwise links to quality of life scales (QOLIE-31-P) and NIH ToolBox behavioral measures. There were significant associations between cluster membership and sociodemographic (handedness, education), cognition (processing speed), and clinical epilepsy factors (presence and lifetime number of generalized tonic-clonic seizures). Further there was a step-wise change in neuroimaging characteristics including cortical thickness (increased extent of regions with decreased cortical thickness) and global graph theory metrics based on morphology and resting state fMRI.
Conclusions: Psychopathology in patients with TLE is characterized by a series of discrete phenotypes that harbor accompanying sociodemographic, clinical and neuroimaging correlates. The underlying neurobiology suggests the degree of psychopathology is linked to increasing abnormal brain networks. Similar to cognition in TLE, machine learning approaches support a developing taxonomy of the comorbidities of epilepsy.
Funding: NIH NINDS R01NS111022; NIH NINDS U01NS093650
Behavior