The frequency, complications and aetiology
of ADHD in new onset paediatric epilepsy
Bruce Hermann,1Jana Jones,1Kevin Dabbs,1Chase A. Allen,1Raj Sheth,1Jason Fine,3Alan McMillan2
and Michael Seidenberg4
1Department of Neurology,2Department of Medical Physics,3Department of Biostatistics,University of Wisconsin School
of Medicine and Public Health, Madison,WI 53792 and4Department of Psychology, Rosalind Franklin University of Medicine
and Science, North Chicago IL,USA
Correspondence to: Dr Bruce Hermann, Matthews Neuropsychology Lab, Department of Neurology,University of
Wisconsin, Madison,WI 53792,USA
Recent studies suggestthat Attention Deficit Hyperactivity Disorder (ADHD) is a common comorbid condition
in childhood epilepsy, but little is known regarding the nature, frequency and timing of associated neurobehav-
ioural/cognitive complications or the underlying aetiology of ADHD in epilepsy.This investigation examined: (i)
the prevalence of ADHD and its subtypes; (ii) the association of ADHD with abnormalities in academic, neu-
ropsychological, behavioural and psychiatric status and (iii) the aetiology of ADHD in paediatric epilepsy.
Seventy-five children (age 8^18) with new/recent onset idiopathic epilepsy and 62 healthy controls underwent
structured interview (K-SADS) to identify the presence and type of DSM-IVdefined ADHD, neuropsychological
assessment, quantitative MR volumetrics, characterization of parent observed executive function, review of
academic/educational progress and assessment of risk factors during gestation and delivery.The results indicate
that ADHD is significantly more prevalent in new onset epilepsy than healthy controls (31% versus 6%), charac-
terized predominantly by the inattentive variant, with onset antedating the diagnosis of epilepsy in the majority
of children. ADHD in childhood epilepsy is associated with significantly increasedrates of school basedremedial
services for academic underachievement, neuropsychological consequences with prominent differences in
executive function, and parent-reported dysexecutive behaviours. ADHD in paediatric epilepsy is neither asso-
ciated with demographic or clinical epilepsy characteristics nor potentialrisk factors during gestation and birth.
Quantitative MRI demonstrates that ADHD in epilepsy is associated with significantly increased gray matter in
distributed regions of the frontal lobe and significantly smaller brainstem volume.Overall, ADHD is a prevalent
comorbidity of new onset idiopathic epilepsy associated with a diversity of salient educational, cognitive, behav-
ioural and social complications that antedate epilepsy onset in a significant proportion of cases, and appear
related to neurodevelopmental abnormalities in brain structure.
Keywords: epilepsy; ADHD
Abbreviations: ADHD=attention deficit hyperactivity disorder; ANOVA=analysis of variance; BRI=behavioural
regulation; BRIEF=behaviour rating inventory of executive function; GEC=global executive composite; IEP=individual
education plan; MCI=metacognition; PD=proton density; VBM=voxel-based morphometry
Received May 8, 2007 . Revised July 28, 2007 . Accepted August 23, 2007
Youth with chronic epilepsy, especially complicated epi-
lepsy, are at increased risk of mental health problems
compared to both the general population and children with
other chronic non-neurological conditions. The Isle of
Wight study (Rutter et al., 1970) documented mental health
problems in 7% of children in the general population, 12%
of children with non-neurological physical disorders, and
significantly higher rates in paediatric epilepsy including
29% in children with uncomplicated and 58% in compli-
cated epilepsy (i.e. structural brain abnormalities and
approximately 30 years later in an independent popula-
tion-based UK epidemiological investigation (Davies et al.,
2003), with further replication and refinement in a large
number of clinical investigations using self-report and
doi:10.1093/brain/awm227Brain (2007) Page1of14
? The Author (2007).Publishedby Oxford University Pressonbehalfofthe Guarantorsof Brain. Allrightsreserved.For Permissions, please email: firstname.lastname@example.org
Brain Advance Access published October 18, 2007
(Noeker et al., 2005).
While the adult epilepsy literature has characterized the
full spectrum of DSM and ICD defined psychiatric
disorders (Swinkels et al., 2005), similar efforts in the
paediatric epilepsy literature have essentially just begun
(Ott et al., 2001; Caplan et al., 2005; McLellan et al., 2005;
Jones et al., 2007), again with a focus on children with
chronic epilepsy. Of the potential psychiatric comorbidities
ofchildhood epilepsy, attention
disorder (ADHD) has been of longstanding interest.
Ounsted (1955) was among the first to call attention to
the syndrome of hyperkinetic disorder and its complica-
tions in children with epilepsy. A growing literature has
characterized disorders of attention in youth with epilepsy
using a diversity of methods including proxy (parent,
teacher) rating scales, behavioural checklists, or formal
cognitive tests (Dunn and Kronenberger, 2005). However,
only three investigations determined the rate of ADHD and
its subtypes in paediatric epilepsy using contemporary
diagnostic criteria that now recognize specific subtypes
of the disorder (DSM-IV). One of these studies was
population based (Hesdorffer et al., 2004) while the
others were derived from tertiary care clinical settings
(Dunn et al., 2003; Sherman et al., 2007). All studies
reported a significantly elevated rate of ADHD in childhood
epilepsy with an overrepresentation of the inattentive
subtype; a distribution that appears different compared
to clinically derived samples of ADHD children seen in
predominates (Barkley, 2006). None of the studies of
ADHD in epilepsy examined the neurobehavioural or
with epilepsy without ADHD or healthy controls.
ADHD affects ?3–7% of all children in the general
population (Rappley, 2005; Polanczyk et al., 2007) and in
clinical populations the majority (80%) are diagnosed with
the combined inattentive, hyperactive and impulsive type;
and a substantially smaller proportion of children are
diagnosed with the predominantly inattentive (10–15%) or
hyperactive and impulsive types (5%) (Rappley, 2005),
although recent population based investigations of DSM-IV
subtypes note that the inattentive type may be at least as
common as the combined type (Graetz et al., 2001;
Barkley, 2006). ADHD is a costly disorder in terms of
direct medical expenditures as well as associated personal
and social consequences (Pelham et al., 2007) given the
relationship of ADHD with learning/education problems
and school failure, poor peer relationships, additional
psychiatric comorbidities (mood, anxiety, conduct dis-
order)and potentialto adversely
including occupational and economic attainment (Wilens
et al., 2002; Barkley, 2006; Spencer et al., 2007). A
diversity of abnormalities in brain structure in ADHD
have been reported including decreased overall cerebral
tissue volumewith preferential
prefrontal region or its asymmetry, cerebellum and/or
posterior–inferior cerebellar vermis, with more variable
reports of atrophy in the corpus callosum or caudate
(Giedd et al., 2001; Castellanos et al., 2002; Sowell et al.,
2003b; Mackie et al., 2007), abnormalities that appear to
be static and non-progressive (Castellanos et al., 2002;
Shaw et al., 2006). Functional imaging studies (fMRI,
FDG-PET) have suggested particular disruption of frontal-
striatal and frontal-parietal circuitry (Dickstein et al.,
2006), which are consistent with the prominence of
investigations of ADHD (Roth and Saykin, 2004).
Importantly, the presence and nature of associated
comorbidities as well as the underlying aetiology and
neurobiology of ADHD in children with epilepsy are issues
that remain to be clarified. Academic, cognitive and
behavioural complications in paediatric epilepsy are often
assumed to be due to the consequences of recurrent
seizures, medical treatment, or fundamental characteristics
of the disorder. The possibility that diverse neurobehav-
ioural problems might bear a close relationship to an
underlying co-occurring disorder such as ADHD has not
been considered.In addition,
suggests that some comorbid disorders, including ADHD
(Hesdorffer et al., 2004; Jones et al., 2007), academic
problems (Oostrom et al., 2003; Berg et al., 2005;
Hermann et al., 2006), depression and suicidal ideation
(Hesdorffer et al., 2006) and behavioural maladjustment
(Austin et al., 2001) may antedate the onset of epilepsy,
comorbidities may represent epiphenomena of underlying
neurobiological abnormalities that remain to be identified.
This would not be surprising in children with symptomatic
or so called complicated epilepsies where early central
nervous system lesions would reasonably result in comorbid
behavioural and cognitive disorders. However, it would be
less expected in idiopathic or uncomplicated epilepsies
where identifiable aetiological insults and neurological
and neuroimaging abnormalities are typically absent.
The purpose of this investigation is to characterize the
rate, type, correlates and aetiology of ADHD in children
with new/recent onset idiopathic epilepsy. Comprehensively
examined are domain-specific neuropsychological status;
the presence, nature and timing of early childhood and
school-based services provided for academic underachieve-
ment; the adequacy of self-directed social, cognitive and
behavioural repertoires dependent on executive function;
and patterns of psychiatric comorbidity. Also critically
examined are issues pertinent to the aetiology of ADHD in
paediatric epilepsy including the timing of onset of ADHD
and its complications in relation to the onset of epilepsy
and the role of an array of potential risk factors
for neurodevelopmental anomalies during gestation and
Page 2 of14 Brain (2007)B. Hermann et al.
abnormalities that may be associated with ADHD in
Research participants included children with new/recent onset
epilepsy (n=75) and healthy first-degree cousin controls (n=62),
aged 8–18 years, all attending regular schools. Children with
epilepsy were recruited from paediatric neurology clinics at two
Midwestern medical centres (University of Wisconsin-Madison,
MarshfieldClinic) and initial
(ii) chronological age between 8–18 years; (iii) no other
developmental disabilities (e.g. autism); (iv) no other neurological
disorder and (v) normal clinical MRI. Epilepsy participants met
criteria for classification of idiopathic epilepsy in that they had
normal neurological examinations, no identifiable lesions on
MR imaging and no other signs or symptoms indicative of
neurological abnormality (Engel, 2001). Control participants were
age and gender-matched first-degree cousins. Criteria for controls
included no histories of: (i) any initial precipitating event
(e.g. simple or complex febrile seizures); (ii) any seizure or
seizure-like episode; (iii) diagnosed neurological disease; (iv) loss
of consciousness greater than 5min; or (v) other family history of
a first-degree related with epilepsy or febrile convulsions.
First-degree cousins were used as controls rather than siblings
or other potential controls groups for the following reasons:
(i) first-degree cousins are more genetically distant from the
participants with epilepsy and thus less pre-disposed than
siblings to shared genetic factors that may contribute to anomalies
in brain structure and cognition; (ii) a greater number of
first-degree cousins are available than siblings in the target age
range and (iii) the family link was anticipated to facilitate
participant recruitmentand especially
(which is our intent) compared to more general control
populations (e.g. unrelated school mates). The IRB approved
recruitment strategy for controls was to ask study participants
and/or parents to identify potential first-degree cousin controls of
the children with epilepsy and initially inquire into the family’s
interest in study participation. The parents of the participants with
epilepsy provided the research coordinator with contact informa-
tion for interested control families and a similar recruitment
process to that described above ensued.
This study was reviewed and approved by the Institutional
Review Boards of both institutions and on the day of study
participation families and children gave informed consent and
assent and all procedures were consistent with the Declaration of
retention over time
Assessment of DSM-IVADHD
Kiddie-SADS-PL (K-SADS), the semi-structured diagnostic inter-
view designed to assess current and past episodes of psycho-
pathology in children and adolescents according to DSM-IV
criteria (Ambrosini, 2000). The K-SADS was completed separately
with the child and parent(s) and summary ratings included all
sources of information in arriving at a diagnosis. Children and
adolescents were interviewed first followed by interview with
parents. Administration of the K-SADS included completion of
the Diagnostic Screening Interview and the appropriate Diagnostic
Supplements. Interviews were videotaped with patient/family
consent and IRB approval. Fifteen percent of subjects were
randomly selected for independent review with an outside
consultant to insure reliability of diagnoses and prevent rater
drift. The interviewer was not blinded to seizure history as
this often arose spontaneously during the interview. Impairment
and/or distress criteria were evaluated in order to accurately reflect
the diagnostic criteria of the DSM-IV. The primary dependent
measures were the rate of lifetime-to-date ADHD and the specific
ADHD subtypes (predominantly inattentive, hyperactive, com-
bined, or NOS). Secondary K-SADS outcome measures included
the rate of other specific Axis I disorders (depressive, anxiety,
psychotic, oppositional defiant/conduct and tic disorders) in order
to characterize any additional psychiatric comorbidity in children
with epilepsy with (ADHD+) or without (ADHD?) ADHD.
An important concern in assessing symptoms of inattention, and
ADHD in particular, is the possible confounding effect of periictal,
postictal or frank ictal activity. Parents were specifically instructed
not to consider symptomatic anything that could be construed as
seizure related phenomena and this was reconfirmed during the
interviews. In addition, one purpose of the independent review of
15% of the interviews was to guard such potential errors and no
diagnostic changes were made through this process.
Finally, medical record review and structured interview by an
independent investigator, blinded to the psychiatric information,
identified the age of diagnosis of epilepsy as well as the date/
timing of the first-recognized seizure as reported either by parent,
observed by proxy (e.g. school nurse), or reported in medical
records and confirmed by parent. The onset of ADHD and special
educational services to be described below were examined in
relation to the first-recognized seizure and formal diagnosis of
epilepsy. Retrospectively dating the actual onset of ADHD can be
a challenge as parental report is not always accurate (Angold et al.,
1996; Barkley, 2006). However, the DSM-IV criteria require that
several symptoms be present prior to age 7 and as the children in
our study were age 8–18, all parental reports involved retro-
spective recall. In that this was a study of new onset epilepsy,
it was not difficult for the parents to consider symptoms as
beginning before versus after the onset of epilepsy.
measures of intelligence, language, immediate and delayed verbal
memory, executive functions and speeded motor/psychomotor
processing. Table 1 overviews the target cognitive domains, the
specific abilities assessed within each domain, the administered
test measuresand the nature of
(i.e. number correct, errors, or time). The raw cognitive test
scores were adjusted for the influence of age (especially important
given the wide age range) and gender. The relationships of age
and gender to test performance were determined in the controls
after excluding a small number of outliers (exceeding?3 SD,
involving 9 of 1116 cells or 0.8%) and similar corrections were
then applied to the epilepsy patients. These age and gender
adjusted z-scores for the individual cognitive tests were then
converted to mean cognitive domain scores as defined in Table 1
battery included clinical
the dependent measure
ADHD in epilepsyBrain (2007) Page 3 of14
psychomotor speed). These procedures serve to reduce the
number of comparisons and experiment-wise Type I error and
also place all cognitive test scores on a common metric so that
relative performance differences across diverse cognitive abilities
can be readily appreciated. All resulting domain scores were
normally distributed (Kolmogorov–Smirnov Test) and examined
for heterogeneity of variance using Levine’s Test prior to group
Most neuropsychological measures are known to be multi-
factorial and assignment to a priori cognitive domains should be
viewed with caution. Our sample size precludes the likelihood
that a stable factor structure for the administered cognitive tests
would be derived. However, most of the measures have been
validated to assess the cognitive constructs we have used.
For interested readers, supplementary files are included that
provide the mean scaled/standard scores for the individual test
measures (supplemental file 1) as well as a table containing
adjusted z-scores and SD for each measure (supplemental file 2)
so that alternative groupings of tests can be considered.
completed questionnaires to characterize the neurodevelopmental,
health, seizure history and behavioural status of their child;
and the mother or other primary caretaker of each child was
administered a brief test of intelligence (WASI 2-subtest). The IQ
of the biological mother was assessed to rule out the possibility
that group differences in the children’s scores might be referable
to systematic variation in maternal intelligence (which was not the
case). Non-biological caretakers (e.g. foster parents) were not
included in this analysis. All medical records pertinent to the
child’s epilepsy and treatment were reviewed after obtaining
appropriate signed consents.
Parents underwent structured interview to characterize the
presence (yes/no) and type of special educational services provided
to children with epilepsy including participation in pre-school
programs (e.g. birth to age 3 or other early childhood programs),
completion of an official individual education plan (IEP),
(e.g. tutoring, summer school). Finally, it was determined whether
these services were provided prior to the diagnosis of epilepsy/
first-recognized seizure (yes/no).
provisionof other supportiveeducationalservices
Behaviourratinginventory ofexecutive function (BRIEF)
To characterize day-to-day executive function, parents completed
the BRIEF (Gioia, 2000), an 86-item parent-report rating scale
with eight theoretically and empirically derived clinical scales that
measure behavioural aspects of executive function. The BRIEF can
be reduced to three overall summary scores which represent
the dependent variables of interest including: (i) behavioural
regulation (BRI) which subsumes specific scales assessing the
ability to control impulses (inhibit); solve problems flexibly and
move from one situation/activity to another as the situation
demands (shift); modulate emotional reactions (emotional con-
trol); (ii) metacognition (MCI) which subsumes specific scales
assessing the ability to begin or activate a task and independently
generate ideas (initiate); hold information in mind/stay with and
stick to an activity (working memory); anticipate future events, set
goals and develop appropriate steps ahead of time to carry out a
task in a systematic manner (plan/organize); keep workspace
and materials in an orderly fashion (organization of materials);
and check work and assess performance to ensure attainment of a
goal (monitor) and (iii) global executive composite (GEC) which
provides a total summary of parent reported executive function.
BRIEF scores are age and gender standardized with a mean of
50 and SD of 10, with higher scores reflecting greater executive
dysfunction. Internal consistency reliability (Chronbach’s alpha) of
the BRIEF is high and ranges from 0.80 (initiate) to 0.98 (GEC)
and evidence for the convergent and divergent validity of the
BRIEF is strong (Gioia, 2000).
Yale neuropsychoeducationalassessment scale
The 60-item subsection of the YNPEAS (Shaywitz, 1982) was
completed by each child’s mother in order to review their health
history during gestation and delivery. This questionnaire provides
T able1 Neuropsychological test battery
Speeded fine motor dexterity
Wechsler Abbreviated Scale of Intelligence (verbal IQ)
Wechsler Abbreviated Scale of Intelligence (performance IQ)a
Delis^Kaplan Executive Function System (letter fluency)b
Children’s Memory Scale (word list^immediate)b
Children’s Memory Scale (word list^delayed)b
Delis^Kaplan Executive Function System (card sort^confirmed)b
Delis^Kaplan Executive Function System (color-word interference test)b
Delis^Kaplan Executive Function System (switching fluency^accuracy)b
Connors’ Continuous PerformanceTest-II (omission errors)c
Connors’ Continuous PerformanceTest-II (commission errors)c
Wechsler Intelligence Scale for Children-III (Digit Symbol Test)b
aSandard scores.bScaled scores.cT-scores.dRaw scores.
Page 4 of14Brain (2007) B. Hermann et al.
complications and composite measures were derived reflecting
medical complications during pregnancy (e.g. hypertension,
rubella, diabetes, toxemia) (12 items), use of prescribed medica-
tions (12 items), adverse health habits (e.g. cigarettes, alcohol)
(3 items),and complications
(e.g. nuchal birth, transfusion) (24 items). Items pertaining to
use of illegal substances were not endorsed and were not further
considered nor were three additional disparate items.
regardingfourmajor dimensions ofpotential
Images were obtained on a 1.5 Tesla GE Signa MR scanner.
Sequences acquired for each participant included: (i) T1-weighted,
three-dimensional SPGR acquired with the following parameters:
TE=5, TR=24, flip angle=40?, NEX=1, slice thickness=1.5mm,
slices=124, plane=coronal, FOV=200, matrix=256?256, (ii)
proton density (PD) and (iii) T2-weighted images acquired with
the following parameters: TE=36ms (for PD) or 96ms (for T2),
TR=3000ms, NEX=1, slice thickness=3.0mm, slices=64, slice
processed using the Brains2 software package (Andreasen et al.,
1996; Harris et al., 1999; Magnotta et al., 1999; Magnotta et al.,
2002). MR processing staff was blinded to the clinical, socio-
The T1-weighted images were resampled to 1.0mm cubic
voxels, then spatially normalized so that the anterior–posterior
axis of the brain was realigned to the ACPC line, and the
interhemispheric fissure was aligned on the other two axes.
A piece-wise linear transformation was defined providing the
ability to warp the standard Talairach atlas space (Talairach, 1988)
onto the resampled image. Images from the three-pulse sequences
were then coregistered using a local adaptation of automated
image registration software. Following alignment of the image sets,
the PD and T2 images were resampled into 1mm cubic voxels
following which an automated algorithm classified each voxel into
gray matter,white matter,
(Harris et al., 1999).
The brains were then ‘removed’ from the skull using a neural
network application that had been trained on a set of manual
traces (Magnotta et al., 2002). Manual inspection and correction
of the output of the neural network tracing was conducted. The
brain images were then volume rendered using local utilities,
producing tissue volumes for regions of interest within the brain.
Because all measurements were obtained in the image space of
the subject and not normalized, ICV was used as a covariate in
the analysis.The dependent
segmented tissue volumes for the frontal, temporal, parietal
and occipital lobes; and total tissue volumes for the cerebellum
Volumetric data could not be obtained for a subset of children
for the following reasons: artefacts due to braces (n=7), excessive
movement (n=8), anxiety which prevented scan completion
(n=5), acquisition errors/technical problems not attributable
to the children (n=4), or processing not completed (n=5).
There was no difference in the rate of unusable scans for children
with epilepsy versus controls (X2=0.79, df=1, P=0.41) or
controls and epilepsy ADHD groups (X2=0.57, df=2, P=0.75).
Scans were available for 46 control, 40 epilepsy ADHD? and 18
epilepsy ADHD+ children.
CSF, blood,or unclassified
variables includedtotal and
Voxel-based morphometry (VBM) (Ashburner and Friston,
2000) was subsequently used to provide greater specificity
regarding the anatomic localization of abnormalities in lobar
tissue volume detected by the above analyses. The VBM procedure
was modified such that the same input data used in the total brain
gray matter analysis could be used. Images were segmented into a
gray matter image using masks defined from the BRAINS2
segmentation procedure which includes a manual cleanup of
the gray matter partition These images were then spatially
normalized to the template space of the SPM2 software
(Wellcome Department of Imaging Neuroscience, University
College, London UK) and voxel intensities were adjusted to
preserve gray matter volume (modulated). The spatially normal-
ized gray matter images were smoothed with a 14-mm FWHM
Gaussian kernel. Analysis of covariance was used to assess
differences between control subjects and the epilepsy subjects
with and without ADHD, using age and total gray matter volume
as covariates, the latter to sensitize the analysis to regional changes
beyond global gray matter differences. To restrict the statistical
analysis to the same regions described in the aforementioned
global gray matter analysis, input data was masked to include
only those voxels used in that analysis, and additionally
masked to restrict the analysis to gray matter regions as
defined from the SPM2a priori gray matter template.
The first set of analyses are primarily descriptive in nature and
involve characterizing the sample, the rate of ADHD in children
with epilepsy versus controls and potential confounding differ-
ences in outcomes for children treated versus untreated (for
ADHD) using two sample t-test for continuous outcomes or tests
for categorical outcomes.The primary analyses focus on assessing
differences among controls and epilepsy ADHD+/? groups on the
core set of variables including cognition (n=5), education history
(n=4), psychiatric status (n=5), BRIEF (n=3), aetiology (n=5)
and MR volumes (n=8). For each endpoint, we first tested
whether there was any difference between the three groups using
F-test from one way analysis of variance (ANOVA) for continuous
outcomes, or tests for differences between two proportions for
binary outcomes in epilepsy ADHD+ versus ADHD? groups
(where control data was not included). Because there are
30 primary endpoints, the determination of statistical significance
was conservatively based on the Bonferroni correction, where
significance occurs only if P-value is less than 0.05/30=0.002. In
the case of ANOVA, this overall test was followed by exploratory
two sample t-tests comparing all pairs of groups (three possible
pairs) using two sample t-test. The resulting P-values should not
be interpreted rigorously in terms of statistical significance
but rather in the context of the exploratory analysis, which is
meant to suggest where differences may be occurring following a
significant F-test. MR analyses were supplemented by region of
interest driven VBM, which was itself corrected for multiple
comparisons as is the convention.
Characterization of ADHD rate and
type in children with epilepsy
Figure 1 provides summary information regarding the
rate and types of DSM-IV defined ADHD in the sample.
ADHD in epilepsyBrain (2007)Page 5 of14
Children with epilepsy exhibited a significantly higher rate
of ADHD (31.5%) compared to controls (6.4%), X2=12.26,
df=1, P<0.001. Among epilepsy ADHD+ children, 52.1%
(12/23) were inattentive subtype, 17.4% (4/23) were
hyperactive subtype, 13.1% (3/23) were combined subtype
and 17.4% (4/23) were NOS subtype. The four children in
the NOS subtype had been diagnosed with ADHD prior to
their participation in the study and development of
epilepsy and three of the four were treated for their
ADHD (Concerta, Ritalin, Strattera). They did not meet the
full criteria for Combined type ADHD (i.e. 10 of 12
required symptoms were endorsed). Due to the fact that
they had a prior diagnosis of ADHD and continued to
exhibit a number of symptoms of ADHD, we felt that
they should be identified as such but classified separately.
A lifetime to date diagnosis of ADHD could be made prior
to seizure onset in 19/23 children (82%). Ten of the
twenty-three epilepsy ADHD+ children presented with
(e.g. Strattera, Adderall, Ritalin, Concerta) compared to
0% of the epilepsy ADHD? group. Among the epilepsy
ADHD+ children there were no significant differences
between those who were treated versus not treated for their
ADHD across the cognitive domain scores (all P>0.29),
BRIEF summary scales (all P>0.61) and all quantitative
volumetric measures (all P>0.13). The four controls with
ADHD were excluded from subsequent analyses. The
epilepsy ADHD cases were first analysed as a group
followed by a very limited number of exploratory analyses
comparing inattentive versus
Table 2 characterizes the clinical and demographic
features of the control and
ADHD+ groups. There were no differences between the
groups (tested by ANOVA) in terms of the following
variables: chronological age (F=1.63, df = 2, 130, P=0.19),
grade (F=2.18, df=2, 130, P=0.12), head circumference
(F=1.95, df=2, 130, P=0.15), or full scale IQ of the
mother (F=1.6, df=2, 124, P=0.21); but full scale IQ of
the children differed (F=12.3, df=2, 130, P<0.001) with
lower IQ in epilepsy ADHD+ compared to controls and
epilepsy ADHD? groups (all P<0.001), but no ADHD?
versus control difference (P=0.26). There was no associa-
tion between epilepsy ADHD+/? and gender (X2=2.9,
df=2, P=0.23), localization related versus idiopathic
generalized epilepsy (X2=0.62, df=1, P=0.43), number
of AED medications (X2=2.96, df=2, P=0.23) or the
duration (P=0.30) or age of onset (P=0.07) of epilepsy.
In summary, a wide range of clinical epilepsy and
ADHD in childhood epilepsy.
were not associatedwith
Correlates and consequences of ADHD
in children with epilepsy
Fig. 2 depicts the lifetime-to-date educational histories of
epilepsy ADHD?/+ groups. Regarding the specifics of
this history, there were no differences between epilepsy
ADHD? and ADHD+ groups in the proportion of children
who participated in early childhood programs (e.g. birth to
three) (17.3% versus 17.4%, z=?0.012, ns). When reaching
school, however, there were differences between epilepsy
ADHD? and ADHD+ groups in the proportion who
completed a formal IEP (15.4% versus 52.2%, z=?5.17,
P<0.001) orwere provided
P<0.001). Compared to epilepsy ADHD? children, these
educational services were more likely to be provided to the
epilepsy ADHD+ group before the formal diagnosis of
Percent of subjects
Percent of subjects
Controls EpilepsyInattentive HyperactiveCombined
Fig.1 ADHD is significantly elevated in youth with epilepsy (left panel) and predominantly characterized by the inattentive subtype
Page 6 of14Brain (2007)B. Hermann et al.
epilepsy (30.8% versus 65.2%, z=?4.4, P<0.001). In
summary, epilepsy ADHD+ is associated with the provision
of educational services to address academic underperfor-
mance, frequently provided before the onset of epilepsy.
Fig. 3 provide a summary of the adjusted (age, gender)
cognitive domain scores (supplemental file 1 provides
group means for the individual cognitive tests). As can be
seen, the epilepsy ADHD+ group performed in a poorer
fashion across all cognitive domains. Because the epilepsy
ADHD+ group had a significantly lower full scale IQ,
the cognitive domain scores were assessed by ANCOVA
with full scale IQ as the covariate, which revealed no
significant group differences in language (F=0.75, df = 2,
125, P=0.47) or verbal memory (F=0.88, df=2, 127,
P=0.42). Significant group differences were observed in
executive function (F=9.6, df=2, 122, P<0.001) where the
epilepsy ADHD+ group scored significantly lower than
controls (P<0.001) and epilepsy ADHD? (P=0.025)
groups and the epilepsy ADHD? group also scoring
lower than controls (P=0.007). Significant group differ-
ences were also evident in motor function (F=12.2, df=2,
126, P<0.001) with the epilepsy ADHD+ group scoring
significantly lower than controls (P<0.001) and epilepsy
ADHD? (P=0.021) groups, with the epilepsy ADHD?
group also significantly worse than controls (P=0.001).
In summary, epilepsy ADHD+ children exhibit dis-
tinct patternsof cognitivemorbidity characterized
T able 2 Demographic and clinical characteristics
Epilepsy ADHD? (n=52)
Epilepsy ADHD+ (n=23)
Head circumference (cm)
Full scale IQ
Parental full scale IQ
Age at diagnosis (years)
Duration of epilepsy (months)
Idiopathic generalized epilepsies
Number of AEDs
106.5 (17 .5)a
52.9 (7 .7)
93.9 (10.7)a, b
Groups with identical superscripts are significantly different (see the text for specific values). Parental full scale IQ controls (n=57);
Parental full scale IQ epilepsy ADHD? (n=51).
Fig. 3 Mean adjusted (age, gender) cognitive domain scores in
controls and epilepsy ADHD+/? groups. Lower scores represent
Percent of Subjects
Fig. 2 Special educational services provided to children with
ADHD in epilepsy Brain (2007) Page 7 of14
predominantly by robust impairments in motor/psycho-
motor speed and executive function.
Parent reported executive function
BRIEF index scores were analysed by ANOVA and all
comparisons were significant including BRI (F=20.1, df=2,
130, P?0.001), GEC (F=33.6, df = 2, 130, P<0.001) and
MCI (F=30.8, df=2, 30, P<0.001). Group differences
were stepwise in nature as shown in Fig. 4 with the epilepsy
ADHD+ group scoring significantly higher (worse) across
all scales compared to the controls (all P<0.001) and
epilepsy ADHD? groups (all P<0.001) while the ADHD?
group, with scores falling in the grossly average range,
differed from controls on the GEC (P=0.003), BRI
(P=0.003) and MCI (P=0.04) indices. The proportion of
children exceeding the recommended clinical cut-off point
(T=65) for the three BRIEF summary scores ranged
between 1.7% to 3.5% for controls, 9.6% to 17.3% for
epilepsy ADHD? and 47.8% to 52.2% for epilepsy
epilepsy ADHD? versus ADHD+ groups are shown in
Fig. 5 where there were no differences in the proportion of
versus 26%, z=?1.31, ns), anxiety disorders (36% versus
34%, z=0.22, ns), tic disorders (7.7% versus 4.3%, z=0.85,
ns) or psychotic disorders (1.9% versus 0.4%, z=?0.86,
ns).There wasa significantly
ADHD+ group (2% versus 30.4%, z=?5.1, P<0.001).
There were no significant differences between groups in the
proportion of epilepsy ADHD? versus ADHD+ groups
endorsing at least one of the medical complications items
during pregnancy (75% versus 71%, z=0.15, ns), use of
prescribed medications (21.1% versus 30.4%, z=?1.31, ns),
use of habit substances (17% versus 9%, z=1.5, ns), or
complications during labour and delivery (23% versus 21%,
Fig. 6 depicts the adjusted (ICV, age) z-scores for total
lobar and cerebellar and brainstem tissue volumes. Adjusted
z-scores were analysed by ANOVA and there were no
significant effects for total temporal lobe (F=1.4, df=2,
100, P=0.25), parietal lobe (F=0.48, df=2, 101, P=0.62),
occipital lobe (F=1.35, df=2, 101, P=0.26) or cerebellum
(F=0.48, df = 2, 101, P=0.62), while significant group
effects were evident for total frontal lobe (F=6.99, df=2,
101, P=0.001) and with a (Bonferroni corrected) trend for
brainstem (F=4.68, df=2, 101, P=0.01) tissue volumes.
Post hoc pair-wise comparisons revealed greater frontal lobe
tissue volume in epilepsy ADHD+ compared to controls
(P=0.013) and epilepsy ADHD? groups (P<0.001) with
no difference betweenADHD?
(P=0.10). Fig. 7 shows the examination of segmented
frontal lobe measurements. Total tissue volume difference
was due to increased gray but not white matter with the
ADHD+ group exhibiting more frontal lobe gray matter
then both controls (P?0.001) and ADHD? children
(P=0.03), with no difference between ADHD? and control
groups (P=0.11). Exploration of total brainstem volume
revealed smaller volume in ADHD+ compared to controls
(P=0.003) and ADHD? groups (P=0.03), again with no
difference between ADHD? and control groups (P=0.29).
Subsequent VBM analysis revealed increased gray matter
in sensorimotor, supplementary motor and prefrontal
regions (Fig. 8).
Fig. 5 Rates of K-SADS defined psychiatric comorbidities
in epilepsy ADHD+/? groups.
Behavioural regulationMetacognition Global executive
BRIEF summary scales
Fig. 4 Mean BRIEF scores in controls and epilepsy ADHD+/?
groups. Higher scores represent greater abnormality.
Page 8 of14Brain (2007)B. Hermann et al.
Four key sets of findings speak to the rate, complications
and aetiology of ADHD in children with epilepsy: (i)
ADHD is a prevalent disorder in children with recent onset
epilepsy characterized predominantly by the inattentive
variant; (ii) ADHD in children with epilepsy is closely
associated with several critical co-occurring problems
including academic underachievement requiring provision
of school-based educational services, neuropsychological
complications and wide ranging problems in day-to-day
behaviours dependent upon executive function; (iii) The
aetiology of ADHD and its complications in epilepsy appear
to have origin prior to the diagnosis of epilepsy and even
the first-recognized seizure in a substantial proportion of
children, but without significant associations with tradi-
tional clinical epilepsy or demographic characteristics,
psychiatric comorbidities (depression/anxiety), or anomalies
during pregnancy and delivery; and (iv) ADHD in
paediatric epilepsy is associated with a distributed pattern
of neurodevelopmental anomalies in brain structure. These
points are discussed below.
Characterization of ADHD rate and
type in children with epilepsy
There is growing interest in the clinical diagnosis of
ADHD and related disorders among children with epilepsy
(Ott et al., 2001; Dunn et al., 2003; Hesdorffer et al., 2004;
Thome-Souza et al., 2004; Dunn and Kronenberger, 2005;
Schubert, 2005; Sherman et al., 2007), due in part to a
longstanding neuropsychological literature that has docu-
mented both static and phasic abnormalities in attention
(Sanchez-Carpintero and Neville, 2003) as well as concern
regarding clinical disorders of attention (Ounsted, 1955).
We confirmed that ADHD is significantly more prevalent
Fig. 8 VBM results showing regions of frontal lobe volume increase in epilepsy ADHD+ relative to controls (yellow), increases relative
to epilepsy ADHD? (green), and increases relative to both groups (red). P<.05, corrected for multiple comparisons.
ICV and age adjusted Z-scores
Fig. 6 Adjusted (age,ICV) z-scores for total lobar, cerebellum and
ICV and age adjusted Z-scores
Fig. 7 Adjusted (age, ICV) z-scores for segmented frontal lobe
ADHD in epilepsyBrain (2007) Page 9 of14
compared to controls (6.4%), with the inattentive subtype
the most common form of the disorder in children with
epilepsy (Fig. 1) as has been reported in previous
studies (Dunn et al., 2003; Hesdorffer et al., 2004;
Sherman et al., 2007).
among childrenwith recentonset epilepsy
Consequences/correlates of ADHD
in children with epilepsy
Investigations of ADHD in children with epilepsy have
focused largely on the rate and type of ADHD and
associated demographic and clinical epilepsy features.
But here we also undertook a thorough assessment of
potential associated neurobehavioural complications of
ADHD (Wilens et al., 2002; Spencer, 2006; Pelham et al.,
2007; Spencer et al., 2007).
Review of history of the supportive academic services and
parental report of academic progress indicate significant
academic/educational complications in new onset epilepsy
ADHD+ children. Compared to epilepsy ADHD? children,
epilepsy ADHD+ children present at the onset of epilepsy
with higher rates of formal individual education plans
(15.4% versus 52.2%) and provision of a diversity of
other in- and out-of-school supportive services (tutors,
summer school, reading programs) (38.5% versus 69.6%)
suggesting a complicated early educational history in these
children when presenting with new/recent onset epilepsy
Academic problems could be due to a variety of factors
including but not limited to behavioural complications of
considerable cognitive disruption in epilepsy ADHD+
children (Fig. 3 and supplementary file 1) with less
adequate performance across all domains of cognitive
motor/psychomotor speed) except memory. When con-
trolled for IQ, motor/psychomotor speed and executive
functions appeared to represent especially salient neuro-
psychological complications in epilepsy ADHD+ children,
characterized by impaired response inhibition, mental
passive inattention. Overall, these results are consistent
with meta-analyses of cognitive deficits in non-epilepsy
ADHD children indicating prominence of impairments
in executive function and other abilities mediated by
frontal-striatal systems (Barkley et al., 1992; Doyle, 2006;
concept formation and
Consistent with the neuropsychological findings, we found
that a diversity of day-to-day behaviours dependent upon
and mediated by higher-level executive functions were
significantly compromised in epilepsy ADHD+ children.
Parents of epilepsy ADHD+ children reported a broad
range of dysexecutive behaviours characterized by decreased
ability to shift cognitive set, modulate emotions and
behaviour through appropriate inhibitory control; and
initiate,plan, organize and
behaviours compared to epilepsy ADHD? and control
groups (Fig. 4). We previously demonstrated that the
inventory used to assess these behaviours, BRIEF, is
significantly related to objective measures of executive
function and thus valid in children with epilepsy (Parrish
et al., 2007). Overall, the degree of executive dysfunction in
epilepsy ADHD+ children is substantial and evident in
both neuropsychological assessment and parent observation.
To underscore the importance of executive dysfunction,
it has been found to be significantly associated with
poorer quality of life in paediatric epilepsy (Sherman
et al., 2006).
sustain problem solving
Aetiology of ADHD in epilepsy
Especially interesting are findings that address the aetiology
of ADHD in epilepsy. To begin, initial characterization of
the demographic or clinical epilepsy features of the groups
(Table 2) revealed no differences between the epilepsy
ADHD+/? groups in terms of seizure syndrome, treat-
ment/non-treatment with AEDs, or age of onset or duration
of epilepsy; nor were there differences in regard to
demographic characteristics including age, gender, or
parental IQ. Subsequent analyses of core outcome measures
revealed no association between the presence/absence of
ADHD in children with epilepsy and lifetime-to-date rates
of DSM-IV mood or anxiety disorders, an important
finding given the potential confounding role these disorders
may have in the diagnosis of ADHD (Barkley, 2006)
Timing of ADHD andits comorbidities
These findings suggest that recurrent seizures and their
treatment may not represent the core aetiological factor
underlying ADHD in children with epilepsy. Care was taken
to date the onset of ADHD in relation to the first-
recognized seizure and the diagnosis of epilepsy. Both
ADHD and its complications (e.g. provision of educational
of epilepsy in the majority (82% for ADHD, 65% for
academics) of cases. These results support the hypothesis
that yet to be identified neurodevelopmental abnormalities
antedate the onset of seizures and contribute to the
development of ADHD and associated comorbidities.
That said, review of educational and developmental history
did not reveal higher rates of participation in 0–3 or other
early childhood programs in epilepsy ADHD+ children
suggesting absence of gross neurodevelopmental delays.
Further, systematic review of risk factors during pregnancy
Page10 of14Brain (2007) B. Hermann et al.
and birth failed to identify any factors that were uniquely
associated with ADHD in epilepsy.
Neurobehaviouraldisorders prior to seizure
onset in animalmodels
Why ADHD and associated comorbidities appear prior
to the onset of recurrent unprovoked seizures in children
with idiopathic epilepsies is a critical issue. Cortez et al.
(2006) reviewed evidence reaffirming that the onset of
recurrent spontaneous seizures is the end result of the
complex process of epileptogenesis which involves a
cascade of transcriptional changes in brain triggered by an
The neurobiological results of these transcriptional changes
include plasticity, apoptosis and further neurogenesis, all of
which could conceivable affect behaviour or cognition prior
to the appearance of overt seizures. While there are a
diversity of animal models of epilepsy including seizure
prone strains (Sarkisian, 2001; Stafstrom et al., 2006),
and preferred models for testing cognition and behaviour
in animals with recurrent seizures (Stafstrom, 2002;
Heinrichs and Seyfried, 2006), it is uncommon for
behavioural or cognitive testing to be conducted prior to
seizure onset which would address the question of whether
neurobehavioural abnormalities may be associated with
support the position of Cortez et al. (2006) including
findings of learning and behavioural abnormalities in the
seizure prone baboon prior to onset of spontaneous
learning impairments in young genetically seizure suscep-
tible rats (F substrain Ihara) prior to onset of spontaneous
seizures (Okaichi et al., 2006); developmental delays,
increased exploratory behaviour and altered habituation
in EL/Suz mice 2 months prior to onset of seizure
susceptibility (McFadyen-Leussis and Heinrichs, 2005),
decreased social investigation in seizure susceptible EL
mice (Turner et al., 2007), and abnormalities in behaviour
and cognition consistent with attentional disturbance in
rat lines selectively bred for differences in amygdala
excitability indexed by fast or slow kindling epileptogenesis
(Anisman and McIntyre, 2002). Thus, neurobehavioural
impairments can be identified in seizure prone strains of
animals prior to seizure onset, presumably related to
processes underlying epileptogenesis, which might prove
pertinent to the disorders evident antecedent to epilepsy
onset in children with epilepsy.
Structural brain abnormalities in epilepsy
For the first time in the epilepsy literature, structural
brain correlates of ADHD are identified. ADHD+ children
exhibit an abnormally increased volume of frontal lobe
gray matter and Bonferroni corrected trend of reduced
total brainstem volumes compared to epilepsy ADHD? and
control children, the latter groups not different from one
another (Figs 6 and 7). Region of interest driven VBM
was then used to search for specific areas of increased gray
matter volume in the frontal lobe of epilepsy ADHD+
children. Regions of increased gray matter volume were
located in sensorimotor, supplementary motor and pre-
frontal regions (Fig. 8), areas congruent with the core
neuropsychological abnormalities in motor/psychomotor
reported dysexecutive behaviours (Anderson, 2002; Stuss
and Levine, 2002; Miller and Cummings, 2007).
Neurodevelopmental processes of cortical pruning and
increasing myelination with
cerebral gray and increasing cerebral white matter volumes
in normally developing children have been elegantly
demonstrated (Giedd et al., 1999; Paus et al., 1999; Sowell
et al., 2003a; Gogtay et al., 2004; Sowell et al., 2004;
Lenroot and Giedd, 2006; Wilke et al., 2006), with a
preponderance of change occurring in the frontal and
parietal regions in late childhood/early adolescence, the
mean age range of the children studied here. The increased
frontal lobe gray matter volumes could be due to an
attenuated rate of frontal lobe cortical pruning or a static
frontal lobe mophometric abnormality. The current data
cannot discriminate between these or other possibilities, but
the history of ADHD and educational/cognitive problems
antedating epilepsy onset might suggest a static abnor-
mality. We will be following these children prospectively
and should be able to address the stability of these
Brainstem volume has been infrequently investigated
in the generalADHD literature
abnormality is this region is provocative, given the role of
the reticular activating system and the origin of several
neurotransmitters in and around the brainstem that have
been linkedto ADHD,and
brainstem pathology and related attentional effects in a
The morphometric abnormalities identified here clearly
differ from those reported in the general population
of children with ADHD including abnormalities in overall
cerebral tissue volume, prefrontal region, cerebellum/
posterior–inferior cerebellar vermis, corpus callosum, and
caudate (Eliez and Reiss, 2000; Hendren et al., 2000; Giedd,
2004; Krain and Castellanos, 2006). The preponderance of
inattentive ADHD in children with epilepsy as well as the
fact that ADHD was comorbid to a primary neurological
disorder are among the factors that could help account for
these differences. Areas of increased gray matter concentra-
tion detected by VBM have been reported in childhood
(Woermann et al., 1999; Betting et al., 2006), but their
association with neurobehavioural abnormalities has not
been examined. While ADHD+ was not associated with
but the trendof
the suggestedrole of
ADHD in epilepsyBrain (2007)Page11of14
specific epilepsy syndromes in this investigation, the link
between increased gray matter and a clinical syndrome
(ADHD), cognitive and behavioural abnormalities are
The limitations of this study require comment. First,
we specifically strove for a comprehensive presentation
of the consequences of ADHD in new onset pediatric
epilepsy, and as a result, a large number of comparisons
were made. The resultant breath of findings conveys a
clearer sense of the impact of ADHD in paediatric epilepsy
than would result from a number of smaller reports
presenting components of the data where issues of multiple
comparisons would not arise. That said, several steps were
taken to reduce the probability of Type I error by
Bonferroni correction. While this approach is conservative,
it rigorously controls for the multiple (n=30) primary
outcomes. Importantly, the conclusions do not change
Of course, there are cases where the loss in power did
lead to lack of significance. An example is brainstem
volume, where the P-value from ANOVA was 0.011.
Here, the means were ?0.00, ?0.24 and ?0.89, with SD
roughly 1, giving an effect size of 0.89 for the resulting
F-test. Using the standard cut-off of 0.05, power to detect
this effect size is 0.69 with 20 per group, 0.87 with 30 per
group and 0.95 with 40 per group, which are comparable to
sample size in our study that is imbalanced. Using the 0.002
cut-off, the powers are 0.24, 0.48 and 0.69 for 20, 30 and
40 per group, respectively,
decreases. Larger sample sizes are needed to have adequate
power to detect such effect sizes with this many outcomes.
Second, the sample size was modest, given the number of
outcomes, especially for epilepsy ADHD+ (n=23). While
the findings seem compelling, particularly given the
stringent criterion used to define statistical significance,
they should be interpreted cautiously. There is a clear need
for replication in larger studies. Third, this study was not
powered to detect differences across ADHD subtypes
(Milich et al., 2001; Barkley, 2006). However, preliminary
comparison of the predominantly inattentive subtype
versus other ADHD subtypes combined on a subset of
the significant findings from each major outcome area
(e.g. executive and motor function for cognition, comple-
tion of individualized education plan for educational
history, BRIEF global executive composite, frontal lobe
volume for MRI) failed to identify any statistically
significant differences. We clearly recognize the limitations
of these subtype analyses, but the overall impact of ADHD
on diverse neurobehavioural outcomes suggests that this is
a very significant comorbidity of pediatric epilepsy deserv-
ing further study. Fourth, it is important note that our case
ascertainment method focused on children and adolescents
with new onset epilepsy; not chronic and intractable
this stringent criterion.
epilepsy or epilepsy complicated by cognitive, educational,
psychiatric, or other comorbidities, a critical difference.
Many of these children were referred back to their primary
care providers for ongoing treatment. Such unique samples
(new onset idiopathic epilepsy with normal intelligence and
normal clinical MRI) appear to provide a unique window
with which to understand the neurobehavioural complica-
tions of pediatric epilepsy.
Finally, from a clinical perspective, Ott et al. (2003) have
called attention to the unmet need for psychiatric treatment
in pediatric epilepsy. Identification of ADHD is clearly
important and the opportunity exists to intervene very
early in the course of epilepsy. The long-term social
prognosis of these children appears to be of considerable
importance and warrants investigation.
This project was supported by NIH NINDS RO1 44351,
F32 MH64988-01A2 and MO1 RR 03186 (GCRC). We
thank Michelle Szomi for overall project coordination;
Dr Ryann Watson for direction regarding classification of
educational services and Adan Myers y Gutierrez, Katherine
Bayless and Karen Wagner for MR processing. We
especially thank Dr. Monica Koehn of Marshfield Clinic
for collaboration and help in recruitment of subjects.
Ambrosini PJ. Historical development and present status of the schedule
for affective disorders and schizophrenia for school-age children
(K-SADS). J Am Acad Child Adolesc Psychiatry 2000; 39: 49–58.
Anderson P. Assessment and development of executive function (EF)
during childhood. Child Neuropsychol 2002; 8: 71–82.
Andreasen NC, Rajarethinam R, Cizadlo T, Arndt S, Swayze VW II,
Flashman LA, et al. Automatic atlas-based volume estimation of human
brain regions from MR images. J Comput Assist Tomogr 1996; 20:
Angold A, Erkanli A, Costello EJ, Rutter M. Precision, reliability and
accuracy in the dating of symptom onsets in child and adolescent
psychopathology. J Child Psychol Psychiatry 1996; 37: 657–64.
Anisman H, McIntyre DC. Conceptual, spatial, and cue learning in the
Morris water maze in fast or slow kindling rats: attention deficit
comorbidity. J Neurosci 2002; 22: 7809–17.
Ashburner J, Friston KJ. Voxel-based morphometry – the methods.
Neuroimage 2000; 11: 805–21.
Austin JK, Harezlak J, Dunn DW, Huster GA, Rose DF, Ambrosius WT.
Behavior problems in children before first recognized seizures. Pediatrics
2001; 107: 115–22.
a handbook for diagnosis and treatment. New York: Guilford; 2006.
Barkley RA, Grodzinsky G, DuPaul GJ. Frontal lobe functions in
attention deficit disorder with and without hyperactivity: a review and
research report. J Abnorm Child Psychol 1992; 20: 163–88.
Berg AT, Smith SN, Frobish D, Levy SR, Testa FM, Beckerman B, et al.
Special education needs of children with newly diagnosed epilepsy. Dev
Med Child Neurol 2005; 47: 749–53.
BettingLE, Mory SB,Li LM,
Guerreiro CA, et al. Voxel-based morphometry in patients with
idiopathic generalized epilepsies. Neuroimage 2006; 32: 498–502.
Caplan R, Siddarth P, Gurbani S, Hanson R, Sankar R, Shields WD.
Depression and anxiety disorders in pediatric epilepsy. Epilepsia 2005;
Page12 of14Brain (2007) B. Hermann et al.
Castellanos FX, Lee PP, Sharp W, Jeffries NO, Greenstein DK, Clasen LS,
et al. Developmental trajectories of brain volume abnormalities in
children and adolescents with attention-deficit/hyperactivity disorder.
JAMA 2002; 288: 1740–8.
Cortez MA, Perez Velazquez JL, Snead OC III. Animal models of
epilepsy and progressive effects of seizures. Adv Neurol 2006; 97:
Davies S, Heyman I, Goodman R. A population survey of mental health
problems in children with epilepsy. Dev Med Child Neurol 2003; 45:
Dickstein SG, Bannon K, Xavier Castellanos F, Milham MP. The neural
correlates of attention deficit hyperactivity disorder: an ALE meta-
analysis. J Child Psychol Psychiatry 2006; 47: 1051–62.
Doyle AE. Executive functions in attention-deficit/hyperactivity disorder.
J Clin Psychiatry 2006; 67 Suppl 8: 21–6.
Dunn DW, Austin JK, Harezlak J, Ambrosius WT. ADHD and epilepsy in
childhood. Dev Med Child Neurol 2003; 45: 50–4.
problems, and ADHD: review and practical considerations. Semin
Pediatr Neurol 2005; 12: 222–8.
Eliez S, Reiss AL. MRI neuroimaging of childhood psychiatric disorders: a
selective review. J Child Psychol Psychiatry 2000; 41: 679–94.
Engel J Jr. A proposed diagnostic scheme for people with epileptic
seizures and with epilepsy: report of the ILAE task force on classification
and terminology. Epilepsia 2001; 42: 796–803.
Giedd JN. Structural magnetic resonance imaging of the adolescent brain.
Ann N Y Acad Sci 2004; 1021: 77–85.
Giedd JN, Blumenthal J, Jeffries NO, Castellanos FX, Liu H, Zijdenbos A,
et al. Brain developmentduring
a longitudinal MRI study. Nat Neurosci 1999; 2: 861–3.
Giedd JN, Blumenthal J, Molloy E, Castellanos FX. Brain imaging of
attention deficit/hyperactivity disorder. Ann N Y Acad Sci 2001; 931:
Gioia GA, Isquith PK, Guy SC, Kenworthy L. Behavior rating inventory of
Assessment Resources, Inc.; 2000.
Gogtay N, Giedd JN, Lusk L, Hayashi KM, Greenstein D, Vaituzis AC,
et al. Dynamic mapping of human cortical development during
childhood through early adulthood. Proc Natl Acad Sci USA 2004;
Graetz BW, Sawyer MG, Hazell PL, Arney F, Baghurst P. Validity of DSM-
IVADHD subtypes in a nationally representative sample of Australian
children and adolescents. J Am Acad Child Adolesc Psychiatry 2001; 40:
HarrisG, AndreasenNC, Cizadlo
Magnotta VA, et al. Improving tissue classification in MRI: a three-
dimensional multispectral discriminant analysis method with automated
training class selection. J Comput Assist Tomogr 1999; 23: 144–54.
Heinrichs SC, Seyfried TN. Behavioral seizure correlates in animal
models of epilepsy: a road map for assay selection, data interpretation,
and the search for causal mechanisms. Epilepsy Behav 2006; 8: 5–38.
Hendren RL, De Backer I, Pandina GJ. Review of neuroimaging studies of
child and adolescent psychiatric disorders from the past 10 years. J Am
Acad Child Adolesc Psychiatry 2000; 39: 815–28.
Hermann B, Jones J, Sheth R, Dow C, Koehn M, Seidenberg M. Children
with new-onset epilepsy: neuropsychological status and brain structure.
Brain 2006; 129: 2609–19.
Hesdorffer DC, Hauser WA, Olafsson E, Ludvigsson P, Kjartansson O.
Depression and suicide attempt as risk factors for incident unprovoked
seizures. Ann Neurol 2006; 59: 35–41.
Hesdorffer DC, Ludvigsson P, Olafsson E, Gudmundsson G, Kjartansson O,
Hauser WA. ADHD as a risk factor for incident unprovoked seizures and
epilepsy in children. Arch Gen Psychiatry 2004; 61: 731–6.
Jones JE, Watson R, Sheth R, Caplan R, Koehn M, Seidenberg M, et al.
Psychiatric comorbidity in children with new onset epilepsy. Dev Med
Child Neurol 2007; 49: 493–7.
Childhood epilepsy, attention
manual. Lutz, FL:Psychological
Krain AL, Castellanos FX. Brain development and ADHD. Clin Psychol
Rev 2006; 26: 433–44.
Lenroot RK, Giedd JN. Brain development in children and adolescents:
insights from anatomicalmagnetic
Biobehav Rev 2006; 30: 718–29.
Mackie S, Shaw P, Lenroot R, Pierson R, Greenstein DK, Nugent TF III,
et al. Cerebellar development and clinical outcome in attention deficit
hyperactivity disorder. Am J Psychiatry 2007; 164: 647–55.
Magnotta VA, Harris G, Andreasen NC, O’Leary DS, Yuh WT, Heckel D.
Structural MR image processing using the BRAINS2 toolbox. Comput
Med Imaging Graph 2002; 26: 251–64.
Magnotta VA, Heckel D, Andreasen NC, Cizadlo T, Corson PW,
Ehrhardt JC, et al. Measurement of brain structures with artificial
neural networks: two- and three-dimensional applications. Radiology
1999; 211: 781–90.
exhibit physical and motor delays and heightened locomotor activity
in response to novelty during development. Epilepsy Behav 2005; 6:
McLellan A, Davies S, Heyman I, Harding B, Harkness W, Taylor D, et al.
Psychopathology in children with epilepsy before and after temporal
lobe resection. Dev Med Child Neurol 2005; 47: 666–72.
Milich R, Balentine AC, Lynam DR. ADHD combined type and
ADHD predominantly inattentive type are distinct and unrelated
disorders. Clin Psychol Sci Pract 2001; 8: 463.
Miller BLe, Cummings JLe. The human frontal lobes: functions and
disorders. New York: The Guilford Press; 2007.
Mirsky AF, Duncan CC. A nosology of disorders of attention. Ann N Y
Acad Sci 2001; 931: 17–32.
Noeker M, Haverkamp-Krois A, Haverkamp F. Development of mental
health dysfunction in childhood epilepsy. Brain Dev 2005; 27: 5–16.
Okaichi Y, Amano S, Ihara N, Hayase Y, Tazumi T, Okaichi H. Open-field
behaviors and water-maze learning in the F substrain of Ihara epileptic
rats. Epilepsia 2006; 47: 55–63.
Jennekens-Schinkel A. Not only a matter of epilepsy: early problems
of cognition and behavior in children with ‘‘epilepsy only’’ – a
Pediatrics 2003; 112: 1338–44.
Ott D, Caplan R, Guthrie D, Siddarth P, Komo S, Shields WD, et al.
Measures of psychopathology in children with complex partial seizures
and primary generalized epilepsy with absence. J Am Acad Child
Adolesc Psychiatry 2001; 40: 907–14.
Ott D, Siddarth P, Gurbani S, Koh S, Tournay A, Shields WD, et al.
Behavioral disorders in pediatric epilepsy: unmet psychiatric need.
Epilepsia 2003; 44: 591–7.
Ounsted C. The hyperkinetic syndrome in epileptic children. Lancet 1955;
Parrish J, Geary E, Jones JE, Sheth R, Hermann BP, Seidenberg M.
Executive functioning in childhood epilepsy: parent-report and cognitive
assessment. Dev Med Child Neurol 2007; 49: 412–6.
Paus T, Zijdenbos A, Worsley K, Collins DL, Blumenthal J, Giedd JN, et al.
Structural maturation of neural pathways in children and adolescents:
in vivo study. Science 1999; 283: 1908–11.
Pelham WE, Foster EM, Robb JA. The economic impact of attention-
deficit/hyperactivity disorder in children and adolescents. Ambul Pediatr
2007; 7: 121–31.
Polanczyk G, de Lima MS, Horta BL, Biederman J, Rohde LA.
The worldwide prevalence of ADHD: a systematic review and
metaregression analysis. Am J Psychiatry 2007; 164: 942–8.
Rappley MD. Attention deficit-hyperactivity disorder. N Engl J Med 2005;
RothRM, Saykin AJ.Executive
hyperactivity disorder: cognitive and neuroimaging findings. Psychiatr
Clin North Am 2004; 27: 83–96, ix.
Rutter M, Graham P, Yule W. A neuropsychiatric study in childhood.
London: S.I.M.P./William Heineman Medical Books; 1970.
study startingat diagnosis.
ADHD in epilepsyBrain (2007) Page13 of14
Sanchez-Carpintero R, Neville BG. Attentional ability in children with Download full-text
epilepsy. Epilepsia 2003; 44: 1340–9.
Sarkisian MR. Overview of the current animal models for human
seizure and epileptic disorders. Epilepsy Behav 2001; 2: 201–16.
Schubert R. Attention deficit disorder and epilepsy. Pediatr Neurol 2005;
Seidman LJ. Neuropsychological functioning in people with ADHD across
the lifespan. Clin Psychol Rev 2006; 26: 466–85.
Shaw P, Lerch J, Greenstein D, Sharp W, Clasen L, Evans A, et al.
Longitudinal mapping of cortical thickness and clinical outcome in
children and adolescents with attention-deficit/hyperactivity disorder.
Arch Gen Psychiatry 2006; 63: 540–9.
ShaywitzSE. TheYale neuropsycho-educational
Schizophr Bull 1982; 8: 360–424.
Sherman EM, Slick DJ, Connolly MB, Eyrl KL. ADHD, neurological
correlates and health-related quality of life in severe pediatric epilepsy.
Epilepsia 2007; 48: 1083–91.
Sherman EM, Slick DJ, Eyrl KL. Executive dysfunction is a significant
predictor of poor quality of life in children with epilepsy. Epilepsia 2006;
Sowell ER, Peterson BS, Thompson PM, Welcome SE, Henkenius AL,
Toga AW. Mapping cortical change across the human life span. Nat
Neurosci 2003a; 6: 309–15.
Sowell ER, Thompson PM, Leonard CM, Welcome SE, Kan E, Toga AW.
Longitudinal mapping of cortical thickness and brain growth in normal
children. J Neurosci 2004; 24: 8223–31.
Sowell ER, Thompson PM, Welcome SE, Henkenius AL, Toga AW,
Peterson BS. Cortical abnormalities in children and adolescents
with attention-deficit hyperactivity
Spencer TJ. ADHD and comorbidity in childhood. J Clin Psychiatry 2006;
67 Suppl 8: 27–31.
disorder. Lancet 2003b;362:
Spencer TJ, Biederman J, Mick E. Attention-deficit/hyperactivity disorder:
diagnosis, lifespan, comorbidities, and neurobiology. Ambul Pediatr
2007; 7: 73–81.
Stafstrom CE. Assessing the behavioral and cognitive effects of seizures on
the developing brain. Prog Brain Res 2002; 135: 377–90.
Stafstrom CE, Moshe SL, Swann
Schwartzkroin PA. Models of pediatric epilepsies: strategies and
opportunities. Epilepsia 2006; 47: 1407–14.
Stuss DT, Levine B. Adult clinical neuropsychology: lessons from studies of
the frontal lobes. Annu Rev Psychol 2002; 53: 401–33.
Swinkels WA, Kuyk J, van Dyck R, Spinhoven P. Psychiatric comorbidity
in epilepsy. Epilepsy Behav 2005; 7: 37–50.
Talairach J, Tournoux P. Co-planar stereotaxic atlas of the human brain.
New York: Thieme Medical; 1988.
Thome-Souza S, Kuczynski E, Assumpcao F Jr, Rzezak P, Fuentes D,
Fiore L, et al. Which factors may play a pivotal role on determining the
type of psychiatric disorder in children and adolescents with epilepsy?
Epilepsy Behav 2004; 5: 988–94.
Turner LH, Lim CE, Heinrichs SC. Antisocial and seizure susceptibility
phenotypes in an animal model of epilepsy are normalized by
impairment of brain corticotropin-releasing factor. Epilepsy Behav 2007;
Weinberger SB, Killam EK. Learning and behavioral abnormalities in the
seizure-prone baboon. Biol Psychiatry 1979; 14: 525–35.
Wilens TE, Biederman J, Spencer TJ. Attention deficit/hyperactivity
disorder across the lifespan. Annu Rev Med 2002; 53: 113–31.
Wilke M, Krageloh-Mann I, Holland SK. Global and local development of
gray and white matter volume in normal children and adolescents. Exp
Brain Res 2007; 178: 296–307.
Woermann FG, Free SL, Koepp MJ, Sisodiya SM, Duncan JS. Abnormal
cerebral structure in juvenile myoclonic epilepsy demonstrated with
voxel-based analysis of MRI. Brain 1999; 122: 2101–8.
JW,Nehlig A, JacobsMP,
Page14 of14 Brain (2007) B. Hermann et al.