The relevance of Spearman's g for epilepsy.

Whilst the concept of a general mental factor known as 'g' has been of longstanding interest, for unknown reasons, it has never been interrogated in epilepsy despite the 100+ year empirical history of the neuropsychology of epilepsy. This investigation seeks to identify g within a comprehensive neuropsychological data set and compare participants with temporal lobe epilepsy to controls, characterize the discriminatory power of g compared with domain-specific cognitive metrics, explore the association of g with clinical epilepsy and sociodemographic variables and identify the structural and network properties associated with g in epilepsy. Participants included 110 temporal lobe epilepsy patients and 79 healthy controls between the ages of 19 and 60. Participants underwent neuropsychological assessment, clinical interview and structural and functional imaging. Cognitive data were subjected to factor analysis to identify g and compare the group of patients with control participants. The relative power of g compared with domain-specific tests was interrogated, clinical and sociodemographic variables were examined for their relationship with g, and structural and functional images were assessed using traditional regional volumetrics, cortical surface features and network analytics. Findings indicate (i) significantly (P < 0.005) lower g in patients compared with controls; (ii) g is at least as powerful as individual cognitive domain-specific metrics and other analytic approaches to discriminating patients from control participants; (iii) lower g was associated with earlier age of onset and medication use, greater number of antiseizure medications and longer epilepsy duration (Ps < 0.04); and lower parental and personal education and greater neighbourhood deprivation (Ps < 0.012); and (iv) amongst patients, lower g was linked to decreased total intracranial volume (P = 0.019), age and intracranial volume adjusted total tissue volume (P = 0.019) and age and intracranial volume adjusted total corpus callosum volume (P = 0.012)-particularly posterior, mid-posterior and anterior (Ps < 0.022) regions. Cortical vertex analyses showed lower g to be associated specifically with decreased gyrification in bilateral medial orbitofrontal regions. Network analysis of resting-state data with focus on the participation coefficient showed g to be associated with the superior parietal network. Spearman's g is reduced in patients, has considerable discriminatory power compared with domain-specific metrics and is linked to a multiplex of factors related to brain (size, connectivity and frontoparietal networks), environment (familial and personal education and neighbourhood disadvantage) and disease (epilepsy onset, treatment and duration). Greater attention to contemporary models of human cognition is warranted in order to advance the neuropsychology of epilepsy.

A longitudinal evaluation of personalized intrinsic network topography and cognitive decline in Parkinson's disease.

Resting state functional magnetic resonance imaging (R-fMRI) offers insight into how synchrony within and between brain networks is altered in disease states. Individual and disease-related variability in intrinsic connectivity networks may influence our interpretation of R-fMRI data. We used a personalized approach designed to account for individual variation in the spatial location of correlation maxima to evaluate R-fMRI differences between Parkinson's disease (PD) patients who showed cognitive decline, those who remained cognitively stable and cognitively stable controls. We compared fMRI data from these participant groups, studied at baseline and 18 months later, using both network-based statistics (NBS) and calculations of mean inter- and intra-network connectivity within pre-defined functional networks. The NBS analysis showed that PD participants who remained cognitively stable showed exclusively (at baseline) or predominantly (at follow-up) increased intra-network connectivity, whereas decliners showed exclusively reduced intra-network and inter- (ventral attention and default mode) connectivity, in comparison with the control group. Evaluation of mean connectivity between all regions of interest (ROIs) within a priori networks showed that decliners had consistently reduced inter-network connectivity for ventral attention, somatomotor, visual and striatal networks and reduced intra-network connectivity for ventral attention network to striatum and cerebellum. These findings suggest that specific functional connectivity covariance patterns differentiate PD cognitive subtypes and may predict cognitive decline. Further, increased intra and inter-network synchrony may support cognitive function in the face of PD-related network disruptions.

Prevalence and localization of nocturnal epileptiform discharges in mild cognitive impairment.

Recent evidence shows that identifying and treating epileptiform abnormalities in patients with Alzheimer's disease could represent a potential avenue to improve clinical outcome. Specifically, animal and human studies have revealed that in the early phase of Alzheimer's disease, there is an increased risk of seizures. It has also been demonstrated that the administration of anti-seizure medications can slow the functional progression of the disease only in patients with EEG signs of cortical hyperexcitability. In addition, although it is not known at what disease stage hyperexcitability emerges, there remains no consensus regarding the imaging and diagnostic methods best able to detect interictal events to further distinguish different phenotypes of Alzheimer's disease. In this exploratory work, we studied 13 subjects with amnestic mild cognitive impairment and 20 healthy controls using overnight high-density EEG with 256 channels. All participants also underwent MRI and neuropsychological assessment. Electronic source reconstruction was also used to better select and localize spikes. We found spikes in six of 13 (46%) amnestic mild cognitive impairment compared with two of 20 (10%) healthy control participants (P = 0.035), representing a spike prevalence similar to that detected in previous studies of patients with early-stage Alzheimer's disease. The interictal events were low-amplitude temporal spikes more prevalent during non-rapid eye movement sleep. No statistically significant differences were found in cognitive performance between amnestic mild cognitive impairment patients with and without spikes, but a trend in immediate and delayed memory was observed. Moreover, no imaging findings of cortical and subcortical atrophy were found between amnestic mild cognitive impairment participants with and without epileptiform spikes. In summary, our exploratory study shows that patients with amnestic mild cognitive impairment reveal EEG signs of hyperexcitability early in the disease course, while no other significant differences in neuropsychological or imaging features were observed among the subgroups. If confirmed with longitudinal data, these exploratory findings could represent one of the first signatures of a preclinical epileptiform phenotype of amnestic mild cognitive impairment and its progression.

T1-/T2-weighted ratio reveals no alterations to gray matter myelination in temporal lobe epilepsy.

Short-range functional connectivity in the limbic network is increased in patients with temporal lobe epilepsy (TLE), and recent studies have shown that cortical myelin content correlates with fMRI connectivity. We thus hypothesized that myelin may increase progressively in the epileptic network. We compared T1w/T2w gray matter myelin maps between TLE patients and age-matched controls and assessed relationships between myelin and aging. While both TLE patients and healthy controls exhibited increased T1w/T2w intensity with age, we found no evidence for significant group-level aberrations in overall myelin content or myelin changes through time in TLE.

Association of neighborhood deprivation with white matter connectome abnormalities in temporal lobe epilepsy.

OBJECTIVE: Social determinants of health, including the effects of neighborhood disadvantage, impact epilepsy prevalence, treatment, and outcomes. This study characterized the association between aberrant white matter connectivity in temporal lobe epilepsy (TLE) and disadvantage using a US census-based neighborhood disadvantage metric, the Area Deprivation Index (ADI), derived from measures of income, education, employment, and housing quality. METHODS: Participants including 74 TLE patients (47 male, mean age = 39.2 years) and 45 healthy controls (27 male, mean age = 31.9 years) from the Epilepsy Connectome Project were classified into ADI-defined low and high disadvantage groups. Graph theoretic metrics were applied to multishell connectome diffusion-weighted imaging (DWI) measurements to derive 162 × 162 structural connectivity matrices (SCMs). The SCMs were harmonized using neuroCombat to account for interscanner differences. Threshold-free network-based statistics were used for analysis, and findings were correlated with ADI quintile metrics. A decrease in cross-sectional area (CSA) indicates reduced white matter integrity. RESULTS: Sex- and age-adjusted CSA in TLE groups was significantly reduced compared to controls regardless of disadvantage status, revealing discrete aberrant white matter tract connectivity abnormalities in addition to apparent differences in graph measures of connectivity and network-based statistics. When comparing broadly defined disadvantaged TLE groups, differences were at trend level. Sensitivity analyses of ADI quintile extremes revealed significantly lower CSA in the most compared to least disadvantaged TLE group. SIGNIFICANCE: Our findings demonstrate (1) the general impact of TLE on DWI connectome status is larger than the association with neighborhood disadvantage; however, (2) neighborhood disadvantage, indexed by ADI, revealed modest relationships with white matter structure and integrity on sensitivity analysis in TLE. Further studies are needed to explore this relationship and determine whether the white matter relationship with ADI is driven by social drift or environmental influences on brain development. Understanding the etiology and course of the disadvantage-brain integrity relationship may serve to inform care, management, and policy for patients.

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.

Application of data harmonization and tract-based spatial statistics reveals white matter structural abnormalities in pediatric patients with focal cortical dysplasia.

Our study assessed diffusion tensor imaging (DTI) metrics of fractional anisotropy (FA), mean diffusivity (MD), and radial diffusivity (RD) in pediatric subjects with epilepsy secondary to Focal Cortical Dysplasia (FCD) to improve our understanding of structural network changes associated with FCD related epilepsy. We utilized a data harmonization (DH) approach to minimize confounding effects induced by MRI protocol differences. We also assessed correlations between DTI metrics and neurocognitive measures of the fluid reasoning index (FRI), verbal comprehension index (VCI), and visuospatial index (VSI). Data (n = 51) from 23 FCD patients and 28 typically developing controls (TD) scanned clinically on either 1.5T, 3T, or 3T-wide-bore MRI were retrospectively analyzed. Tract-based spatial statistics (TBSS) with threshold-free cluster enhancement and permutation testing with 100,000 permutations were used for statistical analysis. To account for imaging protocol differences, we employed non-parametric data harmonization prior to permutation testing. Our analysis demonstrates that DH effectively removed MRI protocol-based differences typical in clinical acquisitions while preserving group differences in DTI metrics between FCD and TD subjects. Furthermore, DH strengthened the association between DTI metrics and neurocognitive indices. Fractional anisotropy, MD, and RD metrics showed stronger correlation with FRI and VSI than VCI. Our results demonstrate that DH is an integral step to reduce the confounding effect of MRI protocol differences during the analysis of white matter tracts and highlights biological differences between FCD and healthy control subjects. Characterization of white matter changes associated with FCD-related epilepsy may better inform prognosis and treatment approaches.

Cerebral cortical thickness and cognitive decline in Parkinson's disease.

In Parkinson's disease (PD), reduced cerebral cortical thickness may reflect network-based degeneration. This study performed cognitive assessment and brain MRI in 30 PD participants and 41 controls at baseline and 18 months later. We hypothesized that cerebral cortical thickness and volume, as well as change in these metrics, would differ between PD participants who remained cognitively stable and those who experienced cognitive decline. Dividing the participant sample into PD-stable, PD-decline, and control-stable groups, we compared mean cortical thickness and volume within segments that comprise the prefrontal cognitive-control, memory, dorsal spatial, and ventral object-based networks at baseline. We then compared the rate of change in cortical thickness and volume between the same groups using a vertex-wise approach. We found that the PD-decline group had lower cortical thickness within all 4 cognitive networks in comparison with controls, as well as lower cortical thickness within the prefrontal and medial temporal networks in comparison with the PD-stable group. The PD-decline group also experienced a greater rate of volume loss in the lateral temporal cortices in comparison with the control group. This study suggests that lower thickness and volume in prefrontal, medial, and lateral temporal regions may portend cognitive decline in PD.

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.

Network, clinical and sociodemographic features of cognitive phenotypes in temporal lobe epilepsy.

This study explored the taxonomy of cognitive impairment within temporal lobe epilepsy and characterized the sociodemographic, clinical and neurobiological correlates of identified cognitive phenotypes. 111 temporal lobe epilepsy patients and 83 controls (mean ages 33 and 39, 57% and 61% female, respectively) from the Epilepsy Connectome Project underwent neuropsychological assessment, clinical interview, and high resolution 3T structural and resting-state functional MRI. A comprehensive neuropsychological test battery was reduced to core cognitive domains (language, memory, executive, visuospatial, motor speed) which were then subjected to cluster analysis. The resulting cognitive subgroups were compared in regard to sociodemographic and clinical epilepsy characteristics as well as variations in brain structure and functional connectivity. Three cognitive subgroups were identified (intact, language/memory/executive function impairment, generalized impairment) which differed significantly, in a systematic fashion, across multiple features. The generalized impairment group was characterized by an earlier age at medication initiation (P < 0.05), fewer patient (P < 0.001) and parental years of education (P < 0.05), greater racial diversity (P < 0.05), and greater number of lifetime generalized seizures (P < 0.001). The three groups also differed in an orderly manner across total intracranial (P < 0.001) and bilateral cerebellar cortex volumes (P < 0.01), and rate of bilateral hippocampal atrophy (P < 0.014), but minimally in regional measures of cortical volume or thickness. In contrast, large-scale patterns of cortical-subcortical covariance networks revealed significant differences across groups in global and local measures of community structure and distribution of hubs. Resting-state fMRI revealed stepwise anomalies as a function of cluster membership, with the most abnormal patterns of connectivity evident in the generalized impairment group and no significant differences from controls in the cognitively intact group. Overall, the distinct underlying cognitive phenotypes of temporal lobe epilepsy harbor systematic relationships with clinical, sociodemographic and neuroimaging correlates. Cognitive phenotype variations in patient and familial education and ethnicity, with linked variations in total intracranial volume, raise the question of an early and persisting socioeconomic-status related neurodevelopmental impact, with additional contributions of clinical epilepsy factors (e.g., lifetime generalized seizures). The neuroimaging features of cognitive phenotype membership are most notable for disrupted large scale cortical-subcortical networks and patterns of functional connectivity with bilateral hippocampal and cerebellar atrophy. The cognitive taxonomy of temporal lobe epilepsy appears influenced by features that reflect the combined influence of socioeconomic, neurodevelopmental and neurobiological risk factors.

Behavioral phenotypes of childhood idiopathic epilepsies.

OBJECTIVE: To characterize the presence and nature of discrete behavioral phenotypes and their correlates in a cohort of youth with new and recent onset focal and generalized epilepsies. METHODS: The parents of 290 youth (age = 8-18 years) with epilepsy (n = 183) and typically developing participants (n = 107) completed the Child Behavior Checklist for children aged 6-18 from the Achenbach System of Empirically Based Assessment. The eight behavior problem scales were subjected to hierarchical clustering analytics to identify behavioral subgroups. To characterize the external validity and co-occurring comorbidities of the identified subgroups, we examined demographic features (age, gender, handedness), cognition (language, perception, attention, executive function, speed), academic problems (present/absent), clinical epilepsy characteristics (epilepsy syndrome, medications), familial factors (parental intelligence quotient, education, employment), neuroimaging features (cortical thickness), parent-observed day-to-day executive function, and number of lifetime-to-date Diagnostic and Statistical Manual of Mental Disorders, 4th edition (DSM-IV) diagnoses. RESULTS: Hierarchical clustering identified three behavioral phenotypes, which included no behavioral complications (Cluster 1, 67% of epilepsy cohort [n = 122]), nonexternalizing problems (Cluster 2, 11% of cohort [n = 21]), and combined internalizing and externalizing problems (Cluster 3, 22% of cohort [n = 40]). These behavioral phenotypes were characterized by orderly differences in personal characteristics, neuropsychological status, history of academic problems, parental status, cortical thickness, daily executive function, and number of lifetime-to-date DSM-IV diagnoses. Cluster 1 was most similar to controls across most metrics, whereas Cluster 3 was the most abnormal compared to controls. Epilepsy syndrome was not a predictor of cluster membership. SIGNIFICANCE: Youth with new and recent onset epilepsy fall into three distinct behavioral phenotypes associated with a variety of co-occurring features and comorbidities. This approach identifies important phenotypes of behavior problem presentations and their accompanying factors that serve to advance clinical and theoretical understanding of the behavioral complications of children with epilepsy and the complex conditions with which they co-occur.

Brain aging in temporal lobe epilepsy: Chronological, structural, and functional.

The association of epilepsy with structural brain changes and cognitive abnormalities in midlife has raised concern regarding the possibility of future accelerated brain and cognitive aging and increased risk of later life neurocognitive disorders. To address this issue we examined age-related processes in both structural and functional neuroimaging among individuals with temporal lobe epilepsy (TLE, N = 104) who were participants in the Epilepsy Connectome Project (ECP). Support vector regression (SVR) models were trained from 151 healthy controls and used to predict TLE patients' brain ages. It was found that TLE patients on average have both older structural (+6.6 years) and functional (+8.3 years) brain ages compared to healthy controls. Accelerated functional brain age (functional - chronological age) was mildly correlated (corrected P = 0.07) with complex partial seizure frequency and the number of anti-epileptic drug intake. Functional brain age was a significant correlate of declining cognition (fluid abilities) and partially mediated chronological age-fluid cognition relationships. Chronological age was the only positive predictor of crystallized cognition. Accelerated aging is evident not only in the structural brains of patients with TLE, but also in their functional brains. Understanding the causes of accelerated brain aging in TLE will be clinically important in order to potentially prevent or mitigate their cognitive deficits.

Neuroanatomical correlates of personality traits in temporal lobe epilepsy: Findings from the Epilepsy Connectome Project.

Behavioral and personality disorders in temporal lobe epilepsy (TLE) have been a topic of interest and controversy for decades, with less attention paid to alterations in normal personality structure and traits. In this investigation, core personality traits (the Big 5) and their neurobiological correlates in TLE were explored using the Neuroticism Extraversion Openness-Five Factor Inventory (NEO-FFI) and structural magnetic resonance imaging (MRI) through the Epilepsy Connectome Project (ECP). NEO-FFI scores from 67 individuals with TLE (34.6 ±â€¯9.5 years; 67% women) were compared to 31 healthy controls (32.8 ±â€¯8.9 years; 41% women) to assess differences in the Big 5 traits (agreeableness, openness, conscientiousness, neuroticism, and extraversion). Individuals with TLE showed significantly higher neuroticism, with no significant differences on the other traits. Neural correlates of neuroticism were then determined in participants with TLE including cortical and subcortical volumes. Distributed reductions in cortical gray matter volumes were associated with increased neuroticism. Subcortically, hippocampal and amygdala volumes were negatively associated with neuroticism. These results offer insight into alterations in the Big 5 personality traits in TLE and their brain-related correlates.

Network analysis of prospective brain development in youth with benign epilepsy with centrotemporal spikes and its relationship to cognition.

OBJECTIVE: Benign epilepsy with centrotemporal spikes (BECTS) is the most common childhood idiopathic localization-related epilepsy syndrome. BECTS presents normal routine magnetic resonance imaging (MRI); however, quantitative analytic techniques have captured subtle cortical and subcortical magnetic resonance anomalies. Network science, including graph theory (GT) analyses, facilitates understanding of brain covariance patterns, potentially informing in important ways how this common self-limiting epilepsy syndrome may impact normal patterns of brain and cognitive development. METHODS: GT analyses examined the developmental covariance among cortical and subcortical regions in children with new/recent onset BECTS (n = 19) and typically developing healthy controls (n = 22) who underwent high-resolution MRI and cognitive assessment at baseline and 2 years later. Global (transitivity, global efficiency, and modularity index [Q]) and regional measures (local efficiency and hubs) were investigated to characterize network development in each group. Associations between baseline-based GT measures and cognition at both time points addressed the implications of GT analyses for cognition and prospective cognitive development. Furthermore, an individual contribution measure was investigated, reflecting how important for cognition it is for BECTS to resemble the correlation matrices of controls. RESULTS: Groups exhibited similar Q and overall network configuration, with BECTS presenting significantly higher transitivity and both global and local efficiency. Furthermore, both groups presented a similar number of hubs, with BECTS showing a higher number in temporal lobe regions compared to controls. The investigated measures were negatively associated with 2-year cognitive outcomes in BECTS. SIGNIFICANCE: Children with BECTS present a higher-than-normal global developmental configuration compared to controls, along with divergence from normality in terms of regional configuration. Baseline GT measures demonstrate potential as a cognitive biomarker to predict cognitive outcome in BECTS 2 years after diagnosis. Similarities and differences in developmental network configurations and their implications for cognition and behavior across common epilepsy syndromes are of theoretical interest and clinical relevance.

Cognitive slowing and its underlying neurobiology in temporal lobe epilepsy.

Cognitive slowing is a known but comparatively under-investigated neuropsychological complication of the epilepsies in relation to other known cognitive comorbidities such as memory, executive function and language. Here we focus on a novel metric of processing speed, characterize its relative salience compared to other cognitive difficulties in epilepsy, and explore its underlying neurobiological correlates. Research participants included 55 patients with temporal lobe epilepsy (TLE) and 58 healthy controls from the Epilepsy Connectome Project (ECP) who were administered a battery of tests yielding 14 neuropsychological measures, including selected tests from the NIH Toolbox-Cognitive Battery, and underwent 3T MRI and resting state fMRI. TLE patients exhibited a pattern of generalized cognitive impairment with very few lateralized abnormalities. Using the neuropsychological measures, machine learning (Support Vector Machine binary classification model) classified the TLE and control groups with 74% accuracy with processing speed (NIH Toolbox Pattern Comparison Processing Speed Test) the best predictor. In TLE, slower processing speed was associated predominantly with decreased local gyrification in regions including the rostral and caudal middle frontal gyrus, inferior precentral cortex, insula, inferior parietal cortex (angular and supramarginal gyri), lateral occipital cortex, rostral anterior cingulate, and medial orbital frontal regions, as well as three small regions of the temporal lobe. Slower processing speed was also associated with decreased connectivity between the primary visual cortices in both hemispheres and the left supplementary motor area, as well as between the right parieto-occipital sulcus and right middle insular area. Overall, slowed processing speed is an important cognitive comorbidity of TLE associated with altered brain structure and connectivity.

Neurobiological substrates of processing speed in childhood epilepsy.

This study investigated the association between processing speed and cortical morphometry in children with idiopathic epilepsies (n = 81) versus healthy controls (n = 57), age 8-18. Participants underwent 1.5 T MRI scanning and cognitive testing including assessment of psychomotor speed (Digit Symbol) at or near the time of epilepsy diagnosis. Vertex analyses of cortical volume, thickness, surface area, and local gyrification index (LGI), as well as volume-based analyses of subcortical structures and cerebellum, were used to determine the morphometric correlates of Digit Symbol performance. Group comparisons revealed that the epilepsy and control groups exhibited different patterns of morphometric association with Digit Symbol performance - controls exhibited several areas of correlation between LGI and psychomotor speed, whereas participants with focal epilepsies exhibited different areas of correlation in different directions, and participants with generalized epilepsy exhibited no correlations. The other cortical morphometric measures showed no regions of significant correlation with Digit Symbol performance. In addition, cerebellum and brain stem volumes correlated with Digit Symbol performance in the control group, but not in epilepsy patients. These results suggest that LGI analysis is able to capture nuanced relationships between features of cortical and subcortical morphology with psychomotor speed, these relationships disrupted in different ways in children with epilepsy.

Progressive dissociation of cortical and subcortical network development in children with new-onset juvenile myoclonic epilepsy.

OBJECTIVE: Structural and functional magnetic resonance imaging (MRI) studies have consistently documented cortical and subcortical abnormalities in patients with juvenile myoclonic epilepsy (JME). However, little is known about how these structural abnormalities emerge from the time of epilepsy onset and how network interactions between and within cortical and subcortical regions may diverge in youth with JME compared to typically developing children. METHODS: We examined prospective covariations of volumetric differences derived from high-resolution structural MRI during the first 2 years of epilepsy diagnosis in a group of youth with JME (n = 21) compared to healthy controls (n = 22). We indexed developmental brain changes using graph theory by computing network metrics based on the correlation of the cortical and subcortical structural covariance near the time of epilepsy and 2 years later. RESULTS: Over 2 years, normally developing children showed modular cortical development and network integration between cortical and subcortical regions. In contrast, children with JME developed a highly correlated and less modular cortical network, which was atypically dissociated from subcortical structures. Furthermore, the JME group also presented higher clustering and lower modularity indices than controls, indicating weaker modules or communities. The local efficiency in JME was higher than controls across the majority of cortical nodes. Regarding network hubs, controls presented a higher number than youth with JME that were spread across the brain with ample representation from the different modules. In contrast, children with JME showed a lower number of hubs that were mainly from one module and comprised mostly subcortical structures. SIGNIFICANCE: Youth with JME prospectively developed a network of highly correlated cortical regions dissociated from subcortical structures during the first 2 years after epilepsy onset. The cortical-subcortical network dissociation provides converging insights into the disparate literature of cortical and subcortical abnormalities found in previous studies.

Psychomotor slowing is associated with anomalies in baseline and prospective large scale neural networks in youth with epilepsy.

Purpose: Psychomotor slowing is a common but understudied cognitive impairment in epilepsy. Here we test the hypothesis that psychomotor slowing is associated with alterations in brain status reflected through analysis of large scale structural networks. We test the hypothesis that children with epilepsy with cognitive slowing at diagnosis will exhibit a cross-sectional and prospective pattern of altered brain development. Methods: A total of 78 children (age 8-18) with new/recent onset idiopathic epilepsies underwent 1.5 T MRI with network analysis of cortical, subcortical and cerebellar volumes. Children with epilepsy were divided into slow and fast psychomotor speed groups (adjusted for age, intelligence and epilepsy syndrome). Results: At baseline, slow-speed performers (SSP) presented lower modularity, lower global efficiency, higher transitivity, and lower number of hubs than fast-speed performers (FSP). Community structure in SSP exhibited poor association between cortical regions and both subcortical structures and the cerebellum while FSP presented well-defined communities. Prospectively, SSP displayed lower modularity but higher global efficiency and transitivity compared to FSP. Modules in FSP showed higher integration between and within themselves compared to SSP. SSP showed hubs mainly from frontal and temporal regions while in FSP were spread among frontal, temporal, parietal, subcortical areas and the left cerebellum. Implications: Results suggest the presence of widespread alterations in large scale networks between fast- and slow-speed children with recent onset epilepsies both at baseline and 2 years later. Slower processing speed appears to be a marker of abnormal brain development antecedent to epilepsy onset as well as brain development over the 2 years following diagnosis.

Brain structure and organization five decades after childhood onset epilepsy.

The purpose of this project was to characterize brain structure and organization in persons with active and remitted childhood onset epilepsy 50 years after diagnosis compared with healthy controls. Participants from a population-based investigation of uncomplicated childhood onset epilepsy were followed up 5 decades later. Forty-one participants had a history of childhood onset epilepsy (mean age of onset = 5.2 years, current chronological age = 56.0 years) and were compared with 48 population-based controls (mean age = 55.9 years). Of the epilepsy participants, 8 had persisting active epilepsy and in 33 the epilepsy had remitted. All participants underwent 3T MRI with subsequent vertex analysis of cortical volume, thickness, surface area and gyral complexity. In addition, cortical and subcortical volumes, including regions of the frontal, parietal, temporal, and occipital lobes, and subcortical structures including amygdala, thalamus, and hippocampus, were analyzed using graph theory techniques. There were modest group differences in traditional vertex-based analyses of cortical volume, thickness, surface area and gyral index, as well as across volumes of subcortical structures, after correction for multiple comparisons. Graph theory analyses revealed suboptimal topological structural organization with enhanced network segregation and reduced global integration in the epilepsy participants compared with controls, these patterns significantly more extreme in the active epilepsy group. Furthermore, both groups with epilepsy presented a greater number of higher Z-score regions in betweenness centrality (BC) than lower Z-score regions compared with controls. Also, contrary to the group with remitted epilepsy, patients with active epilepsy presented most of their high BC Z-score regions in subcortical areas including the amygdala, thalamus, hippocampus, pallidum, and accumbens. Overall, this population-based investigation of long term outcome (5 decades) of childhood onset epilepsy reveals persisting abnormalities, especially when examined by graph theoretical measurements, and provides new insights into the very long-term outcomes of active and remitted epilepsy. Hum Brain Mapp 38:3289-3299, 2017. © 2017 Wiley Periodicals, Inc.

Cognitive phenotypes in childhood idiopathic epilepsies.

OBJECTIVE: The objective of this study was to identify cognitive phenotypes in children with new-onset focal and generalized idiopathic epilepsies and determine their relationship with epilepsy syndrome, brain structure, neurodevelopmental history, and family characteristics. METHODS: One hundred thirty-eight children with new-onset epilepsy and 95 controls (age: 8-18) underwent neuropsychological, clinical, and quantitative MR evaluations. Control participants' neuropsychological data were subjected to confirmatory factor analysis and then resultant factor scores were applied to participants with epilepsy and subjected to latent class analysis. Identified cognitive phenotypes were examined in relation to epilepsy syndrome, quantitative neuroimaging, and familial and neurodevelopmental variables. RESULTS: Confirmatory factor analysis identified five cognitive factors (verbal, perceptual, speed, attention, executive), and latent class analysis identified three clusters of participants with epilepsy: 1) average and similar to controls, 2) mild impairment across multiple cognitive domains, and 3) impairment across all domains with severe attentional impairment, representing 44%, 44%, and 12% of the epilepsy sample, respectively. Cognitive phenotype membership was not associated with epilepsy syndrome but was associated with increasing abnormalities in brain structure, parental IQ, and features of early developmental history. SIGNIFICANCE: Cognitive phenotypes are present in idiopathic childhood epilepsies that are unassociated with traditional epilepsy syndromes but are associated with measures of brain structure, family history, and neurodevelopmental features.