Intelligence and cortical thickness in children with complex partial seizures

Center for Imaging of Neurodegenerative Diseases, San Francisco, CA, USA.
NeuroImage (Impact Factor: 6.36). 07/2011; 57(2):337-45. DOI: 10.1016/j.neuroimage.2011.04.069
Source: PubMed


Prior studies on healthy children have demonstrated regional variations and a complex and dynamic relationship between intelligence and cerebral tissue. Yet, there is little information regarding the neuroanatomical correlates of general intelligence in children with epilepsy compared to healthy controls. In vivo imaging techniques, combined with methods for advanced image processing and analysis, offer the potential to examine quantitative mapping of brain development and its abnormalities in childhood epilepsy. A surface-based, computational high resolution 3-D magnetic resonance image analytic technique was used to compare the relationship of cortical thickness with age and intelligence quotient (IQ) in 65 children and adolescents with complex partial seizures (CPS) and 58 healthy controls, aged 6-18 years. Children were grouped according to health status (epilepsy; controls) and IQ level (average and above; below average) and compared on age-related patterns of cortical thickness. Our cross-sectional findings suggest that disruption in normal age-related cortical thickness expression is associated with intelligence in pediatric CPS patients both with average and below average IQ scores.

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    • "No other volumetric abnormalities were observed in other lobar gray or white matter regions (Hermann et al., 2006b) (Fig. 2). Greater cortical thinning with age in frontal areas has been demonstrated in children and adolescents with complex partial seizures and below average IQ scores compared to healthy controls (Tosun et al., 2011). In children with JME, deficits in executive functions have been associated with significantly smaller thalamic volumes as well as with more frontal CSF, compared to controls and patients with benign childhood epilepsy with centrotemporal spikes (BCECTS) (Pulsipher et al., 2009). "
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    ABSTRACT: Cognitive impairment is the most common comorbidity in children with epilepsy, but its pathophysiology and predisposing conditions remain unknown. Clinical epilepsy characteristics are not conclusive in determining cognitive outcome. Because many children with epilepsy do not have macrostructural magnetic resonance imaging (MRI) abnormalities, the underlying substrate for cognitive impairment may be found at the microstructural or functional level. In the last two decades, new MRI techniques have been developed that have the potential to visualize microstructural or functional abnormalities associated with cognitive impairment. These include volumetric MRI, voxel-based morphometry (VBM), diffusion tensor imaging (DTI), MR spectroscopy (MRS), and functional MRI (fMRI). All of these techniques have shed new light on various aspects associated with, or underlying, cognitive impairment, although their use in epilepsy has been limited and focused mostly on adults. Therefore, in this review, the use of all these different MRI techniques to unravel cognitive impairment in epilepsy is discussed both in adults and children with epilepsy. Volumetric MRI and VBM have revealed significant volume losses in the area of the seizure focus as well as in distant areas. DTI adds evidence of loss of integrity of connections from the seizure focus to distant areas as well as between distant areas. MRS and fMRI have shown impaired function both in the area of the seizure focus as well as in distant structures. For this review we have compiled and compared findings from the various techniques to conclude that cognitive impairment in epilepsy results from a network disorder in which the (micro)structures as well as the functionality can be disturbed.
    Epilepsia 08/2012; 53(10):1690-9. DOI:10.1111/j.1528-1167.2012.03624.x · 4.57 Impact Factor
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    ABSTRACT: Neurobehavioral comorbidities are common in pediatric epilepsy with enduring adverse effects on functioning, but their neuroanatomic underpinning is unclear. Striatal and thalamic abnormalities have been associated with childhood-onset epilepsies, suggesting that epilepsy-related changes in the subcortical circuit might be associated with the comorbidities of children with epilepsy. We aimed to compare subcortical volumes and their relationship with age in children with complex partial seizures (CPS), childhood absence epilepsy (CAE), and healthy controls (HC). We examined the shared versus unique structural-functional relationships of these volumes with behavior problems, intelligence, language, peer interaction, and epilepsy variables in these two epilepsy syndromes. We investigated volumetric differences of caudate, putamen, pallidum, and thalamus in children with CPS (N = 21), CAE (N = 20), and HC (N = 27). Study subjects underwent structural magnetic resonance imaging (MRI), intelligence, and language testing. Parent-completed Child Behavior Checklists provided behavior problem and peer interaction scores. We examined the association of age, intelligence quotient (IQ), language, behavioral problems, and epilepsy variables with subcortical volumes that were significantly different between the children with epilepsy and HC. Both children with CPS and CAE exhibited significantly smaller left thalamic volume compared to HC. In terms of developmental trajectory, greater thalamic volume was significantly correlated with increasing age in children with CPS and CAE but not in HC. With regard to the comorbidities, reduced left thalamic volumes were related to more social problems in children with CPS and CAE. Smaller left thalamic volumes in children with CPS were also associated with poor attention, lower IQ and language scores, and impaired peer interaction. Our study is the first to directly compare and detect shared thalamic structural abnormalities in children with CPS and CAE. These findings highlight the vulnerability of the thalamus and provide important new insights on its possible role in the neurobehavioral comorbidities of childhood-onset epilepsy.
    Epilepsia 12/2013; 54(12):2116-24. DOI:10.1111/epi.12428 · 4.57 Impact Factor
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    ABSTRACT: Purpose Recent studies have correlated neurocognitive function and regional brain volumes in children with epilepsy. We tested whether brain volume differences between children with and without epilepsy explained differences in neurocognitive function. Methods The study sample included 108 individuals with uncomplicated nonsyndromic epilepsy (NSE) and 36 healthy age- and gender-matched controls. Participants received a standardized cognitive battery. Whole brain T1-weighted MRI was obtained and volumes analyzed with FreeSurfer (TM). Key Findings Total brain volume (TBV) was significantly smaller in cases. After adjustment for TBV, cases had significantly larger regional grey matter volumes for total, frontal, parietal, and precentral cortex. Cases had poorer performance on neurocognitive indices of intelligence and variability of sustained attention. In cases, TBV showed small associations with intellectual indices of verbal and perceptual ability, working memory, and overall IQ. In controls, TBV showed medium associations with working memory and variability of sustained attention. In both groups, small associations were seen between some TBV-adjusted regional brain volumes and neurocognitive indices, but not in a consistent pattern. Brain volume differences did not account for cognitive differences between the groups. Significance Patients with uncomplicated NSE have smaller brains than controls but areas of relative grey matter enlargement. That this relative regional enlargement occurs in the context of poorer overall neurocognitive functioning suggests that it is not adaptive. However, the lack of consistent associations between case-control differences in brain volumes and cognitive functioning suggests that brain volumes have limited explanatory value for cognitive functioning in childhood epilepsy.
    Epilepsy research 05/2014; 108(4). DOI:10.1016/j.eplepsyres.2014.02.003 · 2.02 Impact Factor
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