Behavioral phenotypes of childhood idiopathic epilepsies

Long-term characterization of cognitive phenotypes in children with seizures over 36 months

RATIONALE

Children with new-onset epilepsies often exhibit co-morbidities including cognitive dysfunction, which adversely affects academic performance. Application of unsupervised machine learning techniques has demonstrated the presence of discrete cognitive phenotypes at or near the time of diagnosis, but there is limited knowledge of their longitudinal trajectories. Here we investigate longitudinally the presence and progression of cognitive phenotypes and academic status in youth with new-onset seizures as sibling controls.

METHODS

282 subjects (6-16 years) were recruited within 6 weeks of their first recognized seizure along with 167 unaffected siblings. Each child underwent a comprehensive neuropsychological assessment at baseline, 18 and 36 months later. Factor analysis of the neuropsychological tests revealed four underlying domains - language, processing speed, executive function, and verbal memory. Latent trajectory analysis of the mean factor scores over 36 months identified clusters with prototypical cognitive trajectories.

RESULTS

Three unique phenotypic groups with distinct cognitive trajectories over the 36-month period were identified: Resilient, Average, and Impaired phenotypes. The Resilient phenotype exhibited the highest neuropsychological factor scores and academic performance that were all similar to controls; while the Impaired phenotype showed the polar opposite with the worst performances across all test metrics. These findings remained significant and stable over 36 months. Multivariate logistic regression indicated that age of onset, EEG, neurological examination, and sociodemographic disadvantage were associated with phenotype classification.

CONCLUSIONS

This study demonstrates the presence of diverse latent cognitive trajectory phenotypes over 36 months in youth with new-onset seizures that are associated with a stable neuropsychological and academic performance longitudinally.

Impact of sociodemographic disadvantage on neurobehavioral outcomes in children with newly diagnosed seizures and their unaffected siblings over 36 months

OBJECTIVE

This study was undertaken to determine the short-term and longer term impact of sociodemographic disadvantage on the emotional-behavioral status of youths with new onset epilepsy and their unaffected siblings at the time of diagnosis and the subsequent 3 years.

METHODS

Three hundred twelve youths with newly diagnosed epilepsies and 223 unaffected siblings, aged 6-16 years, were independently assessed regarding their emotional and behavioral status by their parents and teachers at baseline, and at 18 at 36 months later; youths with seizures also completed self-report measures of depression, anxiety, and hostility at those three time points. A sociodemographic disadvantage score was computed for each family (children with newly diagnosed seizures and their siblings), and families were separated into four categories from most disadvantaged to least disadvantaged.

RESULTS

In both children and siblings, the least disadvantaged group exhibited the lowest level of neurobehavioral problems, whereas the most disadvantaged group showed a higher level of neurobehavioral problems across all the same behavior metrics. Findings remained stable and significant across all informants (parent, teacher, child) and across all time periods (throughout the 3-year period). Furthermore, both corrected and uncorrected linear regression analyses indicated that disadvantage was a more constant and stable predictor of behavioral and emotional problems over time compared to clinical seizure characteristics and abnormalities in magnetic resonance imaging and electroencephalographic testing.

SIGNIFICANCE

Sociodemographic disadvantage bears a strong relationship to youths with emotional and behavioral problems both at the time of diagnosis as well as prospectively. The relationship is robust and reflected in reports from multiple informants (parent, teacher, child self-report), evident in siblings as well, and possibly more explanatory than traditional clinical seizure variables. Future studies will be needed to determine whether this disadvantage factor is modifiable with early intervention.

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

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-behavioural problems in this patient population. To address these controversies, we take a new person-centred approach through the application of unsupervised machine learning techniques to identify underlying latent groups or behavioural phenotypes. Addressed are the distinct psychopathological profiles, their linked frequency, patterns and severity and the disruptions in morphological and network properties that underlie the identified latent groups. A total of 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 analysed 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 behavioural phenotypes was examined through other measures of behaviour and quality of life. Patients overall exhibited significantly higher (abnormal) scores compared with controls. However, cluster analysis identified three latent groups: (i) unaffected, with no scale elevations compared with controls (Cluster 1, 37%); (ii) mild symptomatology characterized by significant elevations across several Diagnostic and Statistical Manual of Mental Disorders-oriented scales compared with controls (Cluster 2, 42%); and (iii) severe symptomatology with significant elevations across all scales compared with controls and the other temporal lobe epilepsy behaviour phenotype groups (Cluster 3, 21%). Concurrent validity of the behavioural 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 and 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 behavioural phenotype. Psychopathology in these patients is characterized by a series of discrete latent groups that harbour 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.

Characterizing Sleep Phenotypes in Children With Newly Diagnosed Epilepsy

BACKGROUND

Children with epilepsy frequently have sleep, behavior, and cognitive problems at the time of or before the epilepsy diagnosis. The primary goal of this study was to determine if specific sleep disturbance phenotypes exist in a large cohort of children with new-onset epilepsy and if these phenotypes are associated with specific cognitive and behavioral signatures.

METHODS

A total of354 children with new-onset epilepsy, aged six to 16 years, were recruited within six weeks of initial seizure onset. Each child underwent evaluation of their sleep along with self, parent, and teacher ratings of emotional-behavioral status. Two-step clustering using sleep disturbance (Sleep Behavior Questionnaire), naps, and sleep latency was employed to determine phenotype clusters.

RESULTS

Analysis showed three distinct sleep disturbance phenotypes-minimal sleep disturbance, moderate sleep disturbance, and severe sleep disturbance phenotypes. Children who fell into the minimal sleep disturbance phenotype had an older age of onset with the best cognitive performance compared with the other phenotypes and the lowest levels of emotional-behavioral problems. In contrast, children who fell into the severe sleep disturbance phenotype had the youngest age of onset of epilepsy with poor cognitive performance and highest levels of emotional-behavioral problems.

CONCLUSIONS

This study indicates that there are indeed specific sleep disturbance phenotypes that are apparent in children with newly diagnosed epilepsy and are associated with specific comorbidities. Future research should determine if these phenotypic groups persist over time and are predictive of long-term difficulties, as these subgroups may benefit from targeted therapy and intervention.

Behavioral phenotypes of pediatric temporal lobe epilepsy

OBJECTIVE

A broad spectrum of emotional-behavioral problems have been reported in pediatric temporal lobe epilepsy (TLE), but with considerable variability in their presence and nature of expression, which hampers precise identification and treatment. The present study aimed to empirically identify latent patterns or behavioral phenotypes and their correlates.

METHODS

Data included parental ratings of emotional-behavioral status on the Behavior Assessment System for Children, 2nd Edition (BASC-2) of 81 children (mean age = 11.79, standard deviation [SD] = 3.93) with TLE. The nine clinical subscales were subjected to unsupervised machine learning to identify behavioral subgroups. To explore concurrent validity and the underlying composition of the identified clusters, we examined demographic factors, seizure characteristics, psychosocial factors, neuropsychological performance, psychiatric status, and health-related quality of life (HRQoL).

RESULTS

Three behavioral phenotypes were identified, which included no behavioral concerns (Cluster 1, 43% of sample), externalizing problems (Cluster 2, 41% of sample), and internalizing problems (Cluster 3, 16% of sample). Behavioral phenotypes were characterized by important differences across clinical seizure variables, psychosocial/familial factors, everyday executive functioning, and HRQoL. Cluster 2 was associated with younger child age, lower maternal education, and higher rate of single-parent households. Cluster 3 was associated with older age at epilepsy onset and higher rates of hippocampal sclerosis and parental psychiatric history. Both Cluster 2 and 3 demonstrated elevated family stress. Concurrent validity was demonstrated through the association of psychiatric (i.e., rate of Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) disorders and psychotropic medication) and parent-rated HRQoL variables.

SIGNIFICANCE

Youth with TLE present with three distinct behavioral phenotypes that correspond with important clinical and sociodemographic markers. The current findings demonstrate the variability of behavioral presentations in youth with TLE and provide a preliminary framework for screening and targeting intervention to enhance support for youth with TLE and their families.

The Relationship Between Sleep, Cognition and Behavior in Children With Newly-Diagnosed Epilepsy Over 36 Months

Introduction

There is substantial evidence that children with epilepsy experience more sleep, behavior and cognitive challenges than children without epilepsy. However, the literature is limited in describing the relationship between sleep, epilepsy, cognition and behavioral challenges and the interactions amongst these factors over time. This study aims to understand the nature and strength of the relationship between sleep, cognition, mood and behavior in children with new-onset epilepsy as assessed by multiple informants at multiple time periods using multiple different dependent measures.

Methods

332 participants (6-16years) were recruited within 6 weeks of their first recognized seizure. The comparison group was comprised of 266 healthy siblings. Participants underwent sleep evaluation by a parent using the Sleep Behavioral Questionnaire (SBQ), cognitive evaluation using a comprehensive neuropsychological test battery, a behavioral evaluation using the Child Behavior Checklist (CBCL from parents and TRF from teachers) and the Children's Depression Inventory (CDI). These evaluations were completed at baseline (B), at 18 months, and at 36 months.

Results

Compared to siblings, children with new-onset epilepsy had more sleep disturbance (SBQ), higher rates of behavioral problems (CBCL and TRF), lower cognitive testing scores, and higher rates of depression; which persisted over the 36-month study. Sleep significantly correlated with behavioral problems, cognitive scores and depression. When divided into categories based of sleep disturbance scores, 39.7% of children with epilepsy experienced "Persistently Abnormal Sleep", while 14.8% experienced "Persistently Normal Sleep". Children with persistently abnormal sleep experienced the highest rates of behavioral problems, depression and cognitive impairment compared to those with persistently normal sleep, regardless of epilepsy syndrome. Younger age of seizure onset, younger age at testing, and lower grade level at baseline were associated with persistently abnormal sleep.

Conclusions

To our knowledge, this is the first demonstration of the nature, strength, reliability, stability and persistence of the relationship between sleep, cognition, and behavioral problems over time in a large cohort of children with newly diagnosed epilepsy, as assessed by multiple informants at different timepoints. The results of this study indicate that children with epilepsy are at a high risk of significant persisting neurobehavioral multimorbidity. Therefore, early screening for these challenges may be essential for optimizing quality of life long-term.

Neurobehavioural comorbidities of epilepsy: towards a network-based precision taxonomy

Cognitive and behavioural comorbidities are prevalent in childhood and adult epilepsies and impose a substantial human and economic burden. Over the past century, the classic approach to understanding the aetiology and course of these comorbidities has been through the prism of the medical taxonomy of epilepsy, including its causes, course, characteristics and syndromes. Although this 'lesion model' has long served as the organizing paradigm for the field, substantial challenges to this model have accumulated from diverse sources, including neuroimaging, neuropathology, neuropsychology and network science. Advances in patient stratification and phenotyping point towards a new taxonomy for the cognitive and behavioural comorbidities of epilepsy, which reflects the heterogeneity of their clinical presentation and raises the possibility of a precision medicine approach. As we discuss in this Review, these advances are informing the development of a revised aetiological paradigm that incorporates sophisticated neurobiological measures, genomics, comorbid disease, diversity and adversity, and resilience factors. We describe modifiable risk factors that could guide early identification, treatment and, ultimately, prevention of cognitive and broader neurobehavioural comorbidities in epilepsy and propose a road map to guide future research.

Behavioral phenotypes of temporal lobe epilepsy

Objective

To identity phenotypes of self‐reported symptoms of psychopathology and their correlates in patients with temporal lobe epilepsy (TLE).

Method

96 patients with TLE and 82 controls were administered the Symptom Checklist 90‐Revised (SCL‐90‐R) to characterize emotional‐behavioral status. The nine symptom scales of the SCL‐90‐R were analyzed by unsupervised machine learning techniques to identify latent TLE groups. Identified clusters were contrasted to controls to characterize their association with sociodemographic, clinical epilepsy, neuropsychological, psychiatric, and neuroimaging factors.

Results

TLE patients as a group exhibited significantly higher (abnormal) scores across all SCL‐90‐R scales compared to controls. However, cluster analysis identified three latent groups: (1) unimpaired with no scale elevations compared to controls (Cluster 1, 42% of TLE patients), (2) mild‐to‐moderate symptomatology characterized by significant elevations across several SCL‐90‐R scales compared to controls (Cluster 2, 35% of TLE patients), and (3) marked symptomatology with significant elevations across all scales compared to controls and the other TLE phenotype groups (Cluster 3, 23% of TLE patients). There were significant associations between cluster membership and demographic (education), clinical epilepsy (perceived seizure severity, bitemporal lobe seizure onset), and neuropsychological status (intelligence, memory, executive function), but with minimal structural neuroimaging correlates. Concurrent validity of the behavioral phenotype grouping was demonstrated through association with psychiatric (current and lifetime‐to‐date DSM IV Axis 1 disorders and current treatment) and quality‐of‐life variables.

Significance

Symptoms of psychopathology in patients with TLE are characterized by a series of discrete phenotypes with accompanying sociodemographic, cognitive, and clinical correlates. Similar to cognition in TLE, machine learning approaches suggest a developing taxonomy of the comorbidities of epilepsy.