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Keywords:

  • Childhood absence epilepsy;
  • Development;
  • MRI;
  • Frontal lobe;
  • Temporal lobe

Summary

  1. Top of page
  2. Summary
  3. Methods
  4. Results
  5. Discussion
  6. Acknowledgments
  7. References

Purpose: This study compared frontotemporal brain volumes in children with childhood absence epilepsy (CAE) to age- and gender-matched children without epilepsy. It also examined the association of these volumes with seizure, demographic, perinatal, intelligence quotient (IQ), and psychopathology variables.

Methods: Twenty-six children with CAE, aged 7.5–11.8 years, and 37 children without epilepsy underwent brain magnetic resonance imaging (MRI) scans at 1.5 Tesla. Tissue was segmented, and total brain, frontal lobe, frontal parcellations, and temporal lobe volumes were computed. All children had IQ testing and structured psychiatric interviews. Parents provided seizure, perinatal, and behavioral information on each child.

Results: The CAE group had significantly smaller gray matter volumes of the left orbital frontal gyrus as well as both left and right temporal lobes compared to the age- and gender-matched children without epilepsy. In the CAE group these volumes were related to age, gender, ethnicity, and pregnancy complications but not to seizure, IQ, and psychopathology variables. In the group of children without epilepsy, however, the volumes were related to IQ.

Conclusion: These findings suggest that CAE impacts brain development in regions implicated in behavior, cognition, and language. In addition to supporting the cortical focus theory of CAE, these findings also imply that CAE is not a benign disorder.

Recent voxel-based morphometry studies have demonstrated increased anterior thalamic volume (Betting et al., 2006b) and gray matter concentration in the superior mesiofrontal region in adults with absence seizures (Betting et al., 2006a), but thalamic atrophy and reduction of subcortical gray matter volume, increase in right temporal and frontal lobe white matter volumes, and reduced gray matter volume in the thalamus of adolescents and young adults with childhood absence epilepsy (CAE) (Chan et al., 2006; Pardoe et al., 2008). Given possible confounding of long-term effects of duration of the disorder and chronic use of antiepileptic drugs (AEDs) on brain maturation, the study presented herein compared brain volumes in 7.5–11.8-year-old children with CAE to those of age- and gender-matched children without epilepsy.

The study focused on volumes of the frontal lobe, frontal lobe parcellations (i.e., dorsolateral prefrontal gyrus, orbital frontal gyrus, and inferior frontal gyrus), and temporal lobe for four reasons. First, electroencephalography (EEG) studies demonstrate that absence seizures begin with discrete, often unilateral, spikes in the dorsolateral frontal or orbital frontal regions and evolve to engage orbital frontal and mesial frontal regions as well as the temporal lobes during the repeating spike and wave cycles (Holmes et al., 2004; Tucker et al., 2007). Second, the results of imaging studies described previously imply involvement of these brain regions in CAE (Betting et al., 2006a,b; Chan et al., 2006). Third, ongoing development of the frontal and temporal lobes during childhood and adolescence (Sowell et al., 2003b; Gogtay et al., 2004) underscores the importance of examining brain volumes in both these regions in CAE. Fourth, involvement of the orbital frontal gyrus, inferior frontal gyrus, and dorsolateral prefrontal gyrus in behavior/emotions (Sowell et al., 2003a), cognition (Alvarez & Emory, 2006), and language (Szaflarski et al., 2006) suggests that structural abnormalities in these brain regions might be related to the difficulties children with CAE have in these areas of functioning (Williams et al., 1996; Mandelbaum & Burack, 1997; Ott et al., 2001; Pavone et al., 2001; Caplan et al., 2008).

On the basis of prior volumetric (Betting et al., 2006a; Chan et al., 2006) and EEG findings (Holmes et al., 2004; Tucker et al., 2007), albeit in older CAE subjects, we predicted abnormal total brain, frontotemporal, dorsolateral prefrontal gyrus, and orbital frontal gyrus volumes in the CAE group compared to the group without epilepsy. Because age, gender, and IQ (Reiss et al., 1996; Lenroot & Giedd, 2006; Wilke et al., 2007) are related to brain volumes in children without epilepsy, we explored whether the CAE group differed from the nonepilepsy group in the relationship of their brain volumes to these variables.

Within the CAE group, we determined if abnormal frontotemporal volumes were associated with seizure variables and with perinatal complications due to evidence for increased pregnancy complications in pediatric epilepsy (Sidenvall et al., 2001). Finally, the high rate of attention deficit hyperactivity disorder (ADHD) and anxiety disorders in CAE (Caplan et al., 2008), structural and functional abnormalities in the frontal lobe of children without epilepsy who have these psychiatric disorders (De Bellis et al., 2002; Sowell et al., 2003a; McClure et al., 2007), and increased frontal lobe volumes in children with new-onset epilepsy with ADHD (Hermann et al., 2007) underlie the rationale for investigating if psychopathology variables would be associated with CAE frontotemporal volumes.

Methods

  1. Top of page
  2. Summary
  3. Methods
  4. Results
  5. Discussion
  6. Acknowledgments
  7. References

Subjects

The study included 26 children with CAE, aged 7.5–11.8 years with IQ scores of 70 and above, and 37 age- and gender-matched children without epilepsy (Table 1). We determined socioeconomic status using the Hollingshead-2-factor index (Hollingshead, 1973), based on parental occupational and educational status. Mothers provided perinatal data (i.e., number of pregnancy and delivery complications) using a modified version of the Yale Neuropsychoeducational Assessment Scales (Shaywitz, 1982). There were no significant differences in the demographic and perinatal variables of the CAE and nonepilepsy groups.

Table 1.   Demographic features of study groups
 CAEWithout epilepsy
  1. at (61) = 2.70, p < 0.0000.

  2. CAE, childhood absence epilepsy; SD, standard deviation.

n2637
Age (SD) years9.69 (2.14)9.92 (1.98)
Gender (%)
 Male3549
 Female6551
Socioeconomic status (%)
 High (i–iii)2747
 Low (iv–v)7353
Ethnicity (%)
 Caucasian3851
 Noncaucasian6249
Full scale IQ (SD)a105.3 (15.34)116 (13.88)
Perinatal problems (%)
 Delivery2826
 Pregnancy5452
Psychiatric diagnosis (%)540
 Disruptive21NA
 Affective/anxiety17NA
 Disruptive/affective/anxiety12NA
 Other4NA

The primary study inclusion criteria for each CAE subject were a diagnosis of CAE and at least one seizure in the year prior to participation in the study. A pediatric neurologist at each recruitment site diagnosed CAE according to the International League Against Epilepsy Classification (Commission, 1989). All patients with CAE had EEG evidence of 3-Hz spike and wave in addition to absence seizures induced by hyperventilation. We excluded patients with a mixed seizure disorder, previous epilepsy surgery, atypical spike and wave complexes, juvenile myoclonic epilepsy, a neurologic illness other than epilepsy, chronic medical illness, imaging evidence for structural brain abnormalities, a metabolic disorder, a hearing disorder, mental retardation based on school/classroom placement, and bilingual speakers of American English who did not attend English-speaking schools or speak English at home.

We recruited 32% CAE subjects from tertiary centers (i.e., UCLA- and USC-based clinics) and 68% from community services (i.e., Los Angeles and Anaheim Kaiser Permanente, the Los Angeles and San Diego Chapters of the Epilepsy Foundation of America, and private practices). UCLA Institutional Review Board (IRB)–approved recruitment flyers were available for parents of children with CAE at each recruitment site. Interested parents contacted the study coordinator who provided information about the study and used a UCLA IRB–approved telephone script to determine if the children met the study’s inclusionary but none of its exclusionary criteria. The study coordinator also contacted the child’s pediatric neurologist to confirm the child’s diagnosis and to rule out exclusionary criteria. One UCLA pediatric neurology investigator (W.D.S.) reviewed the history, EEG records, and diagnosis of each CAE subject from the different recruitment sites. If he did not concur with the diagnosis or EEG findings, the child was excluded from the study. The parents’ and children’s medical records provided information on seizure variables (Table 2). One CAE subject had slowing on EEG and three had generalized tonic–clonic convulsions in addition to absence seizures.

Table 2.   CAE seizure variables
Seizure variablesCAE
  1. aSeizure control = <5 seizures in year prior to study.

  2. CAE, childhood absence epilepsy; SD, standard deviation.

Log seizure frequency (SD)       6.85 (3.11)
Seizure controla27%
Age of onset (SD) years       6.85 (2.14)
Duration of illness (SD) years       2.23 (2.28)
Antiepileptic drugs
 None8%
 Monotherapy74%
  Valproate12%
  Ethosuximide37%
  Lamotrigine25%
 Polytherapy18%
  Valproate/ethosuximide8%
  Valproate/lamotrigine5%
  Valproate/ethosuximide/lamotrigine5%
Prolonged seizures11%
Febrile seizures8%

To include children from ethnic and socioeconomic status backgrounds similar to those of the patients in the CAE group, we recruited the children without epilepsy from four public and two private Los Angeles schools. The study coordinator screened potential participants for neurologic, psychiatric, language, and hearing disorders through a telephone conversation with a parent. Given the volumetric abnormalities in children without epilepsy who have ADHD (Sowell et al., 2003b), depression (Rosso et al., 2005), and/or anxiety disorder (De Bellis et al., 2002), we excluded children with these diagnoses in the past or who met criteria for these disorders once enrolled in the study.

Procedures

This study was conducted in accordance with the policies of the Human Subjects Protection Committees of the University of California, Los Angeles. Written informed assents and consents were obtained from all subjects and their parents, respectively.

Magnetic resonance imaging (MRI) acquisition

All subjects completed MRI scanning on a 1.5 Tesla GE Signa magnetic resonance imaging scanner (GE Medical Systems, Milwaukee, WI, U.S.A.). The imaging acquisition protocol used to obtain high-resolution three-dimensional (3D) T1-weighted spoiled grass (SPGR) sequences included a sagittal plane acquisition with slice thickness of 1.2 mm, repetition time of 24 ms, echo time of 9 ms, flip angle of 22 degrees, acquisition matrix of 256 × 192, field of view 24, and two excitations. A detailed description of the MRI procedures; image preprocessing; delineation of the prefrontal cortex, dorsolateral frontal cortex/middle frontal gyrus, dorsolateral frontal cortex/superior frontal gyrus, orbital frontal cortex, and temporal lobe; and intra- as well as interrater reliability can be found in Daley et al. (2007).

Cognition

The Wechsler Intelligence Scale for Children–III (WISC-III) (Wechsler, 1991) administered to the children, generated Full Scale, Verbal, and Performance IQ scores.

Psychopathology

The Schedule for Affective Disorders and Schizophrenia for School-Age Children, Present and Lifetime Version (Kaufman et al., 1997) was administered separately to each child and parent by R.C. or a research assistant trained in the administration of the interview. Because the child or parent often talks about the child’s seizures during the interview, these interviewers were not blind to the presence or absence of epilepsy. A second clinician reviewed videotapes of the child interviews and audiotapes of the parent interviews, and a consensus Diagnostic and Statistical Manual of Mental Disorders–4th edition (DSM-IV) diagnosis was reached.

Given the large number of diagnoses relative to the number of subjects in each diagnostic group, we grouped the diagnoses as follows: “disruptive” disorders included ADHD, oppositional defiant disorder, and conduct disorder; “affective/anxiety” disorders included any mood or anxiety disorder; and “mixed” disorders included both “disruptive” and “affective/anxiety” diagnoses.

Data analysis

We compared total brain, gray matter, and white matter volumes between the CAE and nonepilepsy groups using analyses of covariance (ANCOVAs). To compare frontal and temporal gray and white matter volumes in the CAE and nonepilepsy groups, we estimated mixed models using repeated measures with group (CAE, nonepilepsy) as the intersubject and hemisphere (left, right) as the intrasubject classification variable for inferior frontal, orbital frontal, dorsolateral prefrontal (sum of dorsolateral superior and middle frontal cortex), and temporal lobes separately. Total brain volume was included as a covariate for all analyses of volumes other than total brain volume. Demographic (i.e., age, gender, socioeconomic status, and ethnicity) and cognitive (Full Scale IQ) variables were used as covariates in all analyses. For those regions that were significantly different between the two groups, secondary analyses (ANCOVAs with the same covariates as described earlier) were conducted to determine which hemisphere was driving the findings. All tests were two-tailed, and an alpha level of 0.05 was adopted for all inferences.

Within the CAE group, we examined the relationship of volumes to seizure, cognitive, and perinatal variables (i.e., delivery problems, pregnancy problems). In the nonepilepsy group we limited this analysis to demographic, cognitive, and perinatal variables. In investigating the association of volumes with seizure and cognitive variables, we computed mixed linear models for gray and white matter volumes for the temporal lobe, and the following frontal lobe parcellations: dorsolateral prefrontal cortex (sum of dorsolateral superior and middle frontal cortex), inferior frontal, and orbital frontal. Hemisphere (left, right) was used as the intrasubject classification variable. Age, gender, ethnicity, socioeconomic status, seizure variables, Full Scale IQ, and presence of a psychiatric diagnosis (n = 19) were used as predictors. The seizure variables that were used as predictors included age of seizure onset, duration of illness (time from age of onset to participation in study), log seizure frequency, and the number of AEDs subdivided into no AEDs, monotherapy, and polytherapy.

To reduce the number of predictors, we first examined pair-wise relationships between all the predictors of interest and the volumetric measures, and considered only those variables that showed at least a trend (p < 0.1). We then included all these variables as predictors in a stepwise regression model, and determined a smaller subset of predictors that contributed (p < 0.1) to the variance. Then, following the fit of the model from stepwise selection, the importance of each variable included in the model was verified. We also checked for variables, the coefficients of which changed markedly in magnitude when other variables were excluded. We then estimated general linear models using this subset of predictors for each of the regional volumes. The findings from these final models, which included no more than three predictors, are presented in the Results section.

Results

  1. Top of page
  2. Summary
  3. Methods
  4. Results
  5. Discussion
  6. Acknowledgments
  7. References

Between-group differences

Table 3 presents mean frontotemporal volumes in the CAE and nonepilepsy groups. ANCOVAs controlling for total brain volumes demonstrated significantly smaller orbital frontal and temporal lobe gray matter volumes in the CAE compared to the nonepilepsy group with and without demographic and IQ variables in the model. Repetition of this analysis without the three CAE subjects with generalized tonic–clonic convulsions revealed similar findings.

Table 3.   Total brain and frontotemporal volumes in CAE and nonepilepsy groups
Mean volumes (mm3)CAE (SD)Nonepilepsy (SD)
  1. aF1.58 = 4.92, p < 0.03 with total brain volume (TBV) and demographic variables, but not significant when IQ added to the model.

  2. bF1.55 = 7.31, p < 0.009 with TBV, demographic and IQ variables in the model.

  3. cF1.54 = 10.37, p < 0.002 with TBV, demographic and IQ variables in the model.

  4. dF1.55 = 3.79, p < 0.06 with TBV and demographic variables, but not significant when IQ added to the model.

  5. CAE, childhood absence epilepsy; SD, standard deviation.

Total brain1,351.22 (122.46)1,408.15 (143.06)
 Graya763.49 (74.38)810.22 (70.86)
 White470.64 (66.51)484.13 (67.98)
Frontal
 Inferior frontal gray 21.84 (3.83)23.36 (4.73)
 Inferior frontal white10.49 (2.30)11.18 (3/16)
 Orbital frontal grayb32.65 (5.07)36.23 (4.47)
 Orbital frontal white15.81 (3.80)15.39 (2.51)
 Dorsolateral prefrontal gray117.20 (14.44)122.75 (11.15)
 Dorsolateral prefrontal white54.02 (8.39)55.07 (8.16)
Temporal
 Grayc144.35 (15.25)156.42 (15.68)
 Whited66.60 (9.27)67.09 (11.19)

Secondary analyses to determine if the significant between-group volumetric differences were driven by left or right hemisphere volumes demonstrated that compared to the nonepilepsy group, the CAE group had significantly smaller left orbital frontal gray matter volumes (F1.56 = 10.58, p < 0.002) as well as both left (F1.54 = 7.10, p < 0.01) and right temporal lobe gray matter volumes (F1.54 = 10.76, p < 0.002). These findings were robust, with total brain volume, demographic, and IQ variables in the model.

Variables associated with the volumes

CAE

The volumes were associated significantly with age, gender, ethnicity, and pregnancy problems but not with seizure, IQ, and psychopathology variables in the CAE group (Table 4). More specifically, the older children with CAE had significantly larger total, dorsolateral prefrontal, inferior frontal, and temporal lobe white matter volumes than the younger children with CAE. Compared to girls, boys had significantly greater total brain, gray, and white matter volumes; orbital frontal white volumes; and temporal lobe white matter volumes. Caucasian CAE subjects had larger total gray and temporal lobe gray matter volumes than non-Caucasian children did. The CAE children whose mother experienced pregnancy problems had significantly smaller orbital frontal gray matter volumes than those without these problems.

Table 4.   Predictors of CAE volumes
Mean volumesPredictors
Categorical variablesContinuous variables
  1. CAE, childhood absence epilepsy.

Total brainGender: t(24) = 2.84, p < 0.01  Male mean: 1,435.95 (93.07)  Female mean: 1,306.36 (118.68) 
Total grayGender: t(23) = 2.53, p < 0.02  Male mean: 812.24 (49.68)  Female mean: 747.93 (71.32) Ethnicity: t(23) = 2.34, p < 0.03  Caucasian mean: 809.13 (49.80)  Non-Caucasian mean: 751.04 (74.00) 
Total whiteGender: t(23) = 3.40, p < 0.003  Male mean: 513.69 (39.15)  Female mean: 447.85 (65.00)Age: ß = 15.99, t(23) = 3.65, p < 0.002
Inferior frontal gray  
Inferior frontal white Age: ß = 0. 40, t(23) = 2.01, p < 0.06
Orbital frontal grayPregnancy: t(24) = 2.43, p < 0.02  Problems mean: 30.83 (4.53)  No problems mean: 34.87 (4.71) 
Orbital frontal whiteGender: t(24) = 3.02, p < 0.006  Male mean: 18.11 (3.50)  Female mean: 14.31 (3.20) 
Dorsolateral prefrontal gray  
Dorsolateral prefrontal whiteGender: t(23) = 2.17, p < 0.04  Male mean: 58.16 (7.38)  Female mean: 51.82 (8.18)Age: ß = 1.74, t(23) = 2.63, p < 0.02
Temporal grayEthnicity: t(23) = 2.92, p < 0.008  Caucasian mean: 154.72 (14.44)  Non-Caucasian mean: 138.51 (12.66) 
Temporal whiteGender: t(22) = 3.60, p < 0.002  Male mean: 72.95 (7.11)  Female mean: 63.03 (8.51)Age: ß = 2.20, t(22) = 3.48, p < 0.002

Given the significant correlation of age with duration of illness (r = 0.53, p = 0.005), we ascertained if the significant effect of older age was confounded by longer duration of illness in the stepwise regression analyses of the three volumes for which age was a significant predictor (dorsolateral prefrontal gyrus, inferior frontal gyrus, and temporal lobe white matter volumes). To do this, we removed age from the models and included only duration to see if, in the absence of age as a predictor, duration was significant. Duration was not significant (p >0.2) for all three volumes.

Normal

In contrast, age was unrelated to total brain and frontotemporal brain volumes in the control group other than a negative association with dorsolateral prefrontal gray matter volumes (F1.32 = 4.07, p < 0.05). However, larger total brain (F1.34 = 9. 37, p < 0.004), total gray (F1.34 = 5.52, p < 0.03), and total white matter volumes (F1.32 = 1.53, p < 0.002) were positively related to higher IQ scores in the nonepilepsy group. The association of frontotemporal volumes with gender and ethnicity in this group was similar to that of the CAE group.

Discussion

  1. Top of page
  2. Summary
  3. Methods
  4. Results
  5. Discussion
  6. Acknowledgments
  7. References

This first study of frontotemporal volumes in children with CAE demonstrates significantly smaller gray matter volumes of the left orbital frontal gyrus and both temporal lobes compared to age- and gender-matched nonepilepsy children. The lack of an association of these volumetric abnormalities with seizure variables in the CAE group, similar to older CAE patients (Betting et al., 2006a; Chan et al., 2006), implies a role for the neuropathology underlying CAE on brain development from childhood.

These findings have important functional, theoretical, and developmental implications. From the functional perspective, involvement of the frontal and temporal lobes in cognition (Alvarez & Emory, 2006), language (Szaflarski et al., 2006), and behavior/emotions (De Bellis et al., 2002; Sowell et al., 2003a; McClure et al., 2007) suggest that abnormalities in these brain regions might contribute to the reported deficits in these skills in children with CAE (Williams et al., 1996; Mandelbaum & Burack, 1997; Pavone et al., 2001; Henkin et al., 2003, 2005; Caplan et al., 2008). Furthermore, these structural brain abnormalities challenge the theory that CAE is a “benign” disorder.

Among the study’s developmental implications, smaller gray matter volumes in the orbital frontal gyrus and temporal lobes of the CAE group but no significant volumetric differences in the mesial and dorsolateral prefrontal regions, unlike the adult and adolescent studies (Betting et al., 2006a; Chan et al., 2006), might reflect the younger age of our subjects. In addition, the association of smaller orbital frontal gray matter volumes with perinatal complications could imply a specific developmental vulnerability of this brain region in CAE. In support of this explanation, there was no relationship between perinatal difficulties and smaller orbital frontal gyrus gray matter volumes in the control group despite similar rates of pregnancy problems in the CAE and nonepilepsy groups. Furthermore, epidemiologic studies have shown that children whose mothers had pregnancy problems are at increased risk of unprovoked seizures (Sidenvall et al., 2001) and epilepsy (Whitehead et al., 2006).

A third developmental implication is the dissociation in the relationship of frontotemporal volumes with age in the CAE group but with IQ in the normal group. Similar findings in youth who have recent-onset seizures (Hermann et al., 2006) and chronic complex partial seizures (Daley et al., 2007) suggest abnormal maturation patterns in the brains of children with epilepsy with average intelligence compared to children without epilepsy. Although the mean IQ of the nonepilepsy group in our study was significantly higher than that of the CAE group, the relationship between IQ and frontotemporal volumes is similar to other findings in typically developing children (Reiss et al., 1996).

Supporting the left orbital frontal gyrus and bilateral temporal lobe gray matter volume findings in the CAE group, Holmes and colleagues in their source analysis of dense-array, 256-channel scalp EEG studies demonstrated that the onset of absence seizures could be unilateral in the orbital frontal or dorsolateral prefrontal region (Holmes et al., 2004) and that the slow waves of the discharges were restricted to frontotemporal networks, particularly ventromedial frontal networks (Tucker et al., 2007). In fact, localization of CAE spikes (Holmes et al., 2004; Tucker et al., 2007) and abnormal gray matter volumes in the orbital frontal gyrus, together with evidence for the regulatory inhibitory role of this brain region on thalamocortical circuitry via the nucleus reticularis of the thalamus [See review in (Holmes et al., 2004)] bolster the cortical focus theory of CAE [See review in (Meeren et al., 2005)]. Moreover, the lack of an association between orbital frontal gyrus gray matter volumes and seizure variables implies that the underlying neuropathology, not seizures, influences brain development in CAE.

Involvement of the orbital frontal region might also play a role in the comorbidities of CAE (Williams et al., 1996; Mandelbaum & Burack, 1997; Pavone et al., 2001; Henkin et al., 2003, 2005; Caplan et al., 2008). Based on their review of evidence that the orbital frontal region inhibits the thalamoreticular nucleus, mainly in the rostral pole and its association with the limbic, midline, and intralaminar thalamic nuclei, Holmes et al. (2004) suggested that this region regulates alertness, arousal, and motivation. Impairment of these essential functions might, in turn, contribute to the difficulties children with CAE have with cognitive, linguistic, and behavioral/emotional functioning (Williams et al., 1996; Mandelbaum & Burack, 1997; Pavone et al., 2001; Henkin et al., 2003, 2005; Caplan et al., 2008).

Of note, we found no significant differences in the frontal lobe parcellation volumes of the CAE children with and without ADHD in the current study and in children with complex partial seizures (Daley et al., 2007), despite high rates of ADHD in both disorders (Caplan et al., 2004, 2008). Interestingly, children with recent-onset epilepsy with ADHD have significantly larger gray matter volumes of the frontal lobe (Hermann et al., 2007), whereas ADHD children without epilepsy have reduced volume (Castellanos et al., 2002; Durston et al., 2004; Shaw et al., 2006) and cortical thickness (Sowell et al., 2003a) of the frontal lobe. These discrepant findings underscore the need to use similar volumetric and morphometric methods to determine if ADHD in epilepsy involves specific neural circuits and how these vary by epilepsy syndrome and chronicity.

Generalizability of the study findings is restricted by sample-related, study design, and statistical limitations. Regarding the sample, the high rate of uncontrolled seizures in 73% of the CAE subjects reflects the study’s inclusionary criterion that each CAE subject had to have had at least one seizure in the year prior to participation in the study. Of note, 61.5% of study subjects in Chan et al. (2006) and 25% in Betting et al. (2006a) also had uncontrolled seizures. In addition, lack of information on which study subjects had oral or eyelid myoclonia, as well as inclusion of three CAE subjects with generalized tonic–clonic convulsions, could predict an unfavorable prognosis (Grosso et al., 2005) or different course (Incorpora et al., 2002) of CAE. However, removal of these three children from the analyses did not change our findings.

Despite significantly higher IQ scores in the normal group, these were not “supernormal” children, as evident from the previously described similar relationship between IQ and frontotemporal volumes in other normal children (Reiss et al., 1996). Because we controlled for the effects of IQ, the IQ differences between the CAE and subjects without epilepsy did not account for the significantly smaller CAE volumes. Yet, we cannot rule out an association of the volume findings with psychopathology, given the heterogeneity of the epilepsy group in terms of different psychiatric diagnoses and the exclusion of control subjects with a psychiatric diagnosis.

These sample-related limitations underscore the need to replicate our findings on larger CAE samples. As for the study’s data analyses, although we computed multiple statistical tests, they were hypothesis driven, and we reported findings with a p-value below 0.05. Finally, given the study’s cross-sectional design, our developmental conclusions need to be confirmed by a prospective study.

In conclusion, frontotemporal structural abnormalities with volume reduction of the left orbital frontal gyrus and bilateral temporal lobe gray matter suggest that CAE impacts brain development. Localization of these abnormalities in brain regions implicated in behavior/emotions, cognition, and language, also suggest a possible biologic basis for the comorbidities of CAE. In addition to supporting the cortical focus theory of CAE, these findings provide further evidence that CAE is not a benign disorder.

Acknowledgments

  1. Top of page
  2. Summary
  3. Methods
  4. Results
  5. Discussion
  6. Acknowledgments
  7. References

This study was supported by grant NS32070 (R.C.). We appreciate the technical assistance of Erin Lanphier, Ph.D., Pamela Vona, M.A., Keng Nei Wu, B.A., and Lesley Stahl, Ph.D.

We confirm that we have read the Journal’s position on issues involved in ethical publication and affirm that this report is consistent with those guidelines.

Disclosure: None of the authors has any conflict of interest to disclose.

References

  1. Top of page
  2. Summary
  3. Methods
  4. Results
  5. Discussion
  6. Acknowledgments
  7. References