The presence, nature and network characteristics of behavioural phenotypes in temporal lobe epilepsy

Abstract

The relationship between temporal lobe epilepsy and psychopathology has had a long and contentious history with diverse views regarding the presence, nature, and severity of emotional-behavioral problems in this patient population. To address these controversies, we take a new person-centered approach through the application of unsupervised machine learning techniques to identify underlying latent groups, or behavioral phenotypes. Addressed are the distinct psychopathological profiles, their linked frequency, patterns and severity; as well as the disruptions in morphological and network properties that underlie the identified latent groups. 114 patients and 83 controls from the Epilepsy Connectome Project were administered the Achenbach System of Empirically Based Assessment inventory from which six Diagnostic and Statistical Manual of Mental Disorders-oriented scales were analyzed by unsupervised machine learning analytics to identify latent patient groups. Identified clusters were contrasted to controls as well as to each other in order to characterize their association with sociodemographic, clinical epilepsy, and morphological and functional imaging network features. The concurrent validity of the behavioral phenotypes was examined through other measures of behavior and quality of life. Patients overall exhibited significantly higher (abnormal) scores compared to controls. However, cluster analysis identified three latent groups: 1) unaffected, with no scale elevations compared to controls (Cluster 1, 37%), 2) mild symptomatology characterized by significant elevations across several DSM-oriented scales compared to controls (Cluster 2, 42%), and 3) severe symptomatology with significant elevations across all scales compared to controls and the other temporal lobe epilepsy behavior phenotype groups (Cluster 3, 21%). Concurrent validity of the behavioral phenotype grouping was demonstrated through identical stepwise links to abnormalities on independent measures including the National Institutes of Health Toolbox Emotion Battery and quality of life metrics. There were significant associations between cluster membership and sociodemographic (handedness, education), cognition (processing speed), clinical epilepsy (presence and lifetime number of tonic-clonic seizures), and neuroimaging characteristics (cortical volume and thickness and global graph theory metrics of morphology and resting-state functional MRI). Increasingly dispersed volumetric abnormalities and widespread disruptions in underlying network properties were associated with the most abnormal behavioral phenotype. Psychopathology in these patients is characterized by a series of discrete latent groups that harbor accompanying sociodemographic, clinical, and neuroimaging correlates. The underlying neurobiological patterns suggest that the degree of psychopathology is linked to increasingly dispersed abnormal brain networks. Similar to cognition, machine learning approaches support a novel developing taxonomy of the comorbidities of epilepsy.

Normative and individual, non-normative intrinsic networks and the transition to impaired cognition

“…In short, a key point in our data is that highly individualized networks were abnormal in TLE patients before they showed significant cognitive impairment. This spectrum perspective on the key cognitive phenotypes(Bell et al, 2011; Hermann et al, 2021; Struck et al, 2023) likely has implications for other disorders of cognitive impairment, perhaps even for normal aging. That is, highly individual, personal brain networks arising from an individual’s unique experiences and learning may possess an earlier vulnerability to brain disease compared to canonical brain networks.…”

“…Previous studies have shown that cognitive status in TLE can be characterized by several cognitive phenotypes, involving one or more groups with expected cognitive anomalies, but also a group with an unexpected, intact cognitive profile. (Elverman et al, 2019; Hermann et al, 2020; Hermann et al, 2021; Reyes et al, 2019; Struck et al, 2023) In this context, a direct comparison of intrinsic brain systems in TLE patients with distinct neurocognitive profiles could have significant implications. We argue that investigating TLE patients with “intact” versus “impaired” neurocognitive profiles, interrogating for the presence of macroscopic intrinsic connectivity networks (normative and highly individual), characterizing the organizational properties of each, and, lastly, testing for a spectrum shift in these networks when transitioning from “intact-to-impaired” cognitive status,(Tracy et al, 2021) will help us understand how cognitively “intact” TLE patients support their cognitive functioning while other TLE patients display lost functionality.…”

Postoperative seizure and memory outcome of temporal lobe epilepsy with hippocampal sclerosis: A systematic review

“…In addition, the influence of coexistent mental disorders must be considered, including anxiety, depression, and apathy. Several studies have focused on the impairment of several cognitive domains and have detected distinct groups with unique clinical and cognitive phenotypes and neuroimaging profiles in patients with focal epilepsy 71–73 . Following the DSM‐5 system and the IC‐CoDE approach, TLE with HS could also be divided into different groups, such as intact cognitive phenotype, subjective cognitive decline, and memory‐impaired with and without impairment of other domains.…”