- Email: [email protected]
Mapping the landscape of cognitive development in children with epilepsy
Accepted Manuscript Mapping the landscape of cognitive development in children with epilepsy Tanja S. Kellermann, PhD, Leonardo Bonilha, MD PhD, Jack J. Lin, MD, Bruce P. Hermann, PhD PII:
S0010-9452(15)00058-1
DOI:
10.1016/j.cortex.2015.02.001
Reference:
CORTEX 1395
To appear in:
Cortex
Received Date: 10 September 2014 Revised Date:
2 February 2015
Accepted Date: 6 February 2015
Please cite this article as: Kellermann TS, Bonilha L, Lin JJ, Hermann BP, Mapping the landscape of cognitive development in children with epilepsy, CORTEX (2015), doi: 10.1016/j.cortex.2015.02.001. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
ACCEPTED MANUSCRIPT Landscape of cognitive development in children with epilepsy
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Tanja S. Kellermann PhD1, Leonardo Bonilha MD PhD1, Jack J. Lin MD2, Bruce P. Hermann PhD3 1 Department of Neurosciences, Medical University of South Carolina, Charleston, SC; 2 Department of Neurology, University of California Irvine, Irvine, CA; 3 Department of Neurology, University of Wisconsin, Madison, WI
Manuscript word count: 3296 words Abstract word count: 248 words Figures: 3 Tables: 2
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Running head: Landscape of cognitive development in children with epilepsy
Key words: cognitive development, cognitive network, early-onset epilepsy, graph theory, neuropsychological assessment
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Disclosure: The authors report no financial or nonfinancial conflicts of interest associated with this study.
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Mapping the landscape of cognitive development in children with epilepsy
Address correspondence to:
Bruce P. Hermann PhD Matthews Neuropsychology Lab Department of Neurology, University of Wisconsin School of Medicine and Public Health 7223 UW Medical Foundation Centennial Building 1685 Highland Ave Madison, WI 53705-2281 Phone: (608) 263-5430 (Neuropsychology Lab) Fax: (608) 265-0172 email: [email protected]
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Abstract
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Objective: Normal childhood development is defined by age-dependent improvement across
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cognitive abilities, including language, memory, psychomotor speed and executive function.
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Epilepsy is often associated with a global disruption in cognitive development, however, it is
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still largely unknown how epilepsy affects the overall organization of overlapping cognitive
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domains. The aim of the study was to evaluate how childhood epilepsy affects the
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developmental interrelationships between cognitive domains.
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Methods: We performed a comprehensive assessment of neuropsychological function in 127
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children with new onset epilepsy and 80 typically developing children matched for age, gender,
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and socio-demographic status. A cross-correlation matrix between the performances across
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multiple cognitive tests was used to assess the interrelationship between cognitive modalities
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for each group (patients and controls). A weighted network composed by the cognitive domains
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as nodes, and pair-wise domain correlation as links, was assessed using graph theory analyses,
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with focus on global network structure, network hubs and community structure.
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Results: Normally developing children exhibited a cognitive network with well-defined
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modules, with verbal intelligence, reading and spelling skills occupying a central position in the
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developing network. Conversely, children with epilepsy demonstrated a less well-organized
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network with less clear separation between modules, and relative isolation of measures of
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attention and executive function.
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Conclusion: Our findings demonstrate that childhood-onset epilepsy, even within its early
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course, is associated with an extensive disruption of cognitive neurodevelopmental
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organization. The approach used in this study may be useful to assess the effectiveness of
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future interventions aimed at mitigating the cognitive consequences of epilepsy.
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1. Introduction Normal cognitive development is characterized by the - age dependent harmonious
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improvement across multiple cognitive abilities. There is now considerable agreement that
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childhood onset epilepsy disrupts this systematic developmental process with adverse impact
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on executive function (Bell, Lin, Seidenberg, & Hermann, 2011; D'Agati, Cerminara, Casarelli,
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Pitzianti, & Curatolo, 2012; Seidenberg et al., 1986), academic skills (Berg et al., 2005;
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Seidenberg et al., 1986), linguistic abilities (Caplan et al., 2008; Caplan et al., 2009) and
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emotional/behavioral quality of life (Berg et al., 2008; C. Camfield, Breau, & Camfield, 2003; P.
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Camfield & Camfield, 2003). However, cognitive domains do not operate in isolation and it
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remains undefined how epilepsy influences the interactions among different
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neuropsychological domains.
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Recent cognitive training studies support the notion that the interrelationship among domains is crucial for academic success in school-aged children. First, fluid intelligence and
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problem solving skills can be improved when children engage in working memory exercises
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(Jaeggi, Buschkuehl, Jonides, & Shah, 2011). Second, training in executive function enhances
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other domains including language and mathematical skills (Goldin et al., 2014). Therefore, the
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understanding of the inter-relationship among cognitive domains can provide crucial insight on
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how childhood-onset epilepsy interferes with the global structure of cognitive development. It
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may also aid in the identification of strategies for future targeted intervention. In this study, we employed graph theory to investigate the impact of epilepsy on the
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global cognitive landscape. Graph theory is a mathematical method that has recently been
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applied to examine brain network structure in epilepsy, revealing global disruption in brain
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architecture (Bonilha et al., 2013; Bonilha et al., 2012; Vaessen et al., 2014; Vaessen et al.,
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2012). Large –scale structural morphometrical brain changes have been correlated with specific
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cognitive deficits in epilepsy (Alexander, Concha, Snyder, Beaulieu, & Gross, 2014; Bonilha,
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Tabesh, et al., 2014). However, to date, there has not been a comprehensive examination of
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neuropsychological measures, as a cognitive network per se, using graph theory. To our
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knowledge, this study is the first to use graph theory measures to investigate the cognitive
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network in children with epilepsy.
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We investigated the global cognitive network in a large cohort of children with newonset epilepsy and healthy controls by examining the inter-correlations of 23
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neuropsychological measures. The neuropsychological tests were selected to provide a broad
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profile of cognitive function, indexing abilities in six discrete cognitive domains: intelligence,
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academic performance, language, memory, executive function, and psychomotor speed.
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We hypothesized that epilepsy may disrupt age-related cognitive development, which is manifested in altered interactions among different cognitive domains. We therefore expected
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to find a different arrangement of the cognitive network in children with epilepsy compared
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with healthy controls.
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2.1. Subjects
We assessed data from 127 children with epilepsy (mean age: 12.31 years; SD=3.17; girls=67, boys=60)) and 80 healthy controls (mean age: 12.69 years; SD=3.17; girls=39,
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boys=60). There were no significant differences between the epilepsy and control families in
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terms of parent intelligence quotient (IQ; Wechsler Abbreviated Scale of Intelligence, controls:
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109.7 ± 12.22, patients: 110.82 ± 15.41, p = 0.80), or parental employment status (full-time,
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part-time, unemployed; mother's, chi-square = 4.72, p = 0.19; father's, chi-square = 2.68, p =
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0.44), indicating similar socioeconomic status between the groups. The educational
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background was also similar between patients and controls (patient mean grade= 6.2, control
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mean grade= 6.4, p=0.69. About 53% of patients with epilepsy and 20% of controls received
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educational services in the past. All subjects included in this study were enrolled in regular
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schools.
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Inclusion criteria for the patient group were: 1) diagnosis of epilepsy within the past 12
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months, 2) no other neurological disorders, 3) normal neurological examinations, and 4) normal
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clinical imaging results. A board- certified pediatric neurologist (blinded to neuropsychological
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and interview data) confirmed that all patients had idiopathic epilepsies and provided
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independent confirmation of syndrome diagnosis. All patients were diagnosed according to the 4
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criteria defined by the International League Against Epilepsy (ILAE) (Blume et al., 2001; Engel,
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2006). We excluded children with intellectual disability (IQ < 70), autism, and/or other
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neurological disorders. Specifics regarding the participant selection process have been
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described in detail in a previous publication (Hermann et al., 2006). In general, we tried to
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stay true to the concept of “epilepsy only” as defined broadly in the literature: normal
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neurological exams, average intelligence, and attendance at regular schools.
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Healthy controls were age- and gender-comparable first-degree cousins of the epilepsy participants. All controls had no history of seizures, early initial precipitating injuries (e.g.,
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febrile convulsions), other developmental or neurologic disease, or episodes of loss of
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consciousness. Research approval was obtained from the Institutional Review Board at the
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University of Wisconsin Medical School. Written informed consent was obtained for all subjects
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from legal guardians. Parents underwent a clinical interview and completed questionnaires to
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characterize gestation, delivery, neurodevelopment, and seizure history. All pertinent medical
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records were obtained after signed release of information was obtained from the parent.
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Parents were also interviewed through a structured questionnaire about their child’s school
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progress and any specific educational services provided to address academic problems (AP).
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These services included the traditional individualized educational plan (IEP) as well as early
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childhood interventions (e.g., speech, physical or occupational therapy), mandatory summer
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school, grade retention, special tutoring services, and other related services. This interview was
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conducted blinded to cognitive and behavioral results.
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Table 1 about here
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2.2. Neuropsychological Assessment A neuropsychological test battery involving 23 different cognitive tests was
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administered to all subjects, all of which are listed in Table 2. This battery assessed multiple
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domains including intelligence, academic skills, language, memory, executive function and
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cognitive/psychomotor speed. The measures listed in Table 2 came from the following tests:
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Wechsler Abbreviated Scale of Intelligence (WASI) (Wechsler, 2003), Peabody Picture
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Vocabulary Test (PPVT) (Dunn & Hottel, 1961), Expressive Vocabulary Test (EVT) (Williams, 5
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1997), Wide Range Achievement Test-IV (WRAT-IV, 2006), Wechsler Intelligence Scale for
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Children (WISC-IV) (Wechsler, 2003), Continuous Performance Task (CPT), (Conners, 1992),
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Delis-Kaplan Executive Function System (D-KEFS) (Delis, 2001), and Boston Naming Test (BNT)
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(Kaplan, 1983)).
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5 Table 2 about here 2.3. Network Analysis
In order to define the inter-relationship between cognitive domains, we assessed a network of cognitive functions defined as the correlation matrix between performances on the
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neuropsychological tests (separately for each group, i.e., patients and controls). Specifically, a
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correlation coefficient was calculated between each possible pair of tests, resulting in 253
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possible test combinations. A correlation table was constructed based on these associations,
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representing an undirected (i.e., symmetrical along the diagonal) weighted adjacency matrix for
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each group (patients and controls).
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Prior to being submitted to the correlations, all tests were adjusted to reflect a higher score for better performance. After the adjacency matrices were constructed, only positive
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correlation coefficients were maintained to enable the visual representation of networks and
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graph theory metric calculations. Thus, the cognitive network was comprised of 23 nodes, each
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one representing each cognitive test, and weighted links between nodes representing the
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strength of positive correlation between each pair of nodes.
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2.3.1. Visual representation of network structure
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In order to appreciate the overall structure of cognitive development in each group, we
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reconstructed two-dimensional graphs based on the correlation adjacency matrices. The data
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were exported to the software Gephi (http://gephi.github.io/), where they were displayed
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using a Force Atlas algorithm (Jacomy, Venturini, Heymann, & Bastian, 2014) (attraction
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strength =10, repulsion strength =100, gravity=30). To improve visualization of the network
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structure, we reconstructed the graphs based on networks containing only the links above the
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70% percentile of weight in each group, i.e., links below the 70% percentile were given
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weight=0, while the remaining links maintained their original weights. The use of a fixed density 6
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threshold based on link weight was chosen in order to preserve the overall network
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architecture, while preserving only the strongest and more meaningful links (Bonilha, Nesland,
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Rorden, & Fridriksson, 2014; van Wijk, Stam, & Daffertshofer, 2010; Zalesky et al., 2011).
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The community structure of each network was calculated using the software Gephi and each node was coded in accordance with module participation. Nodes within a module are
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those with a higher strength of inter-relationship, with relative lower relationship with nodes
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outside the modules.
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2.3.2. Global network parameters
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Graph theory metrics were employed to evaluate global network properties. These calculations were performed using the Brain Connectivity Toolbox within the software MATLAB
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(Rubinov & Sporns, 2010). For each group, we assessed network small worldness by evaluating
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the relation between the normalized whole-network clustering coefficient and the normalized
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whole-network characteristic path length, as described by Brown et al (Brown et al., 2011). The
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normalized whole-network clustering coefficient was defined as the ratio between the
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clustering coefficient of the observed network and the average clustering coefficient from 100
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random networks; similarly, the normalized whole-network characteristic path length was
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defined as the ratio between the path-length of the observed network and the average path-
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length from 100 random networks.
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betweenness centrality, mean degree and mean network efficiency were calculated. In order to
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enable a quantitative assessment of each metric per group, we assessed the distribution of
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node-wise correlation by calculating an adjacency matrix from cognitive data where the values
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were resampled with replacement (i.e., bootstrapped). This was repeated 1000 times, yielding
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1000 adjacency matrices per group. For consistency, only positive entries were maintained.
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Global network metrics were assessed for each bootstrapped matrix in each group, and
differences in dispersion across groups were calculated employing t-tests.
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2.3.3. Local network parameters
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To investigate the participation of nodes within the same community structure, we calculated the network modularity using a well-defined stochastic test of modularity (Rubinov &
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Sporns, 2010). With this approach, module classification may vary slightly between calculations
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due to the variations in the probability distributions of local and global minima. This approach
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confers the advantage of enabling the frequency with which nodes are classified as belonging
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to the same module. Thus, after repeating the calculation of modularity 1000 times for each
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group, we calculated the frequency of co-association of nodes within a module (irrespective of
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the module label). For example, 2 nodes could have been placed in the same module 90% of
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the times in patients, but only 20% in controls. These frequencies were compared using a series
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of Chi-squared tests to evaluate differences in link-wise associations in patients versus controls.
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For all quantitative analyses, the level of statistical significant was set of p<0.05 adjusted
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for multiple comparisons utilizing Bonferroni correction.
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3. Results
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3.1. Visual representation of network structure
The adjacency matrices representing the cross-correlations between tests are demonstrated in Figure 1.
Figure 1 about here
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The two-dimensional graphs illustrating the structure of the cognitive network for each group is
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demonstrated in Figure 2.
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Figure 2 about here
From a qualitative perspective, there were remarkable differences in network
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arrangement between groups. While controls demonstrated an organized separation between
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functional modules, patients exhibited a poorly organized pattern, with significant overlap
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between multiple modalities.
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In controls, there was a clear separation between modules composed mostly of executive function tests, verbal and arithmetic performance, memory and attention. Non8
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verbal intelligence served as the interface between verbal performance and executive function
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modules, while speed and response inhibition represented the interface between executive
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function and verbal skills. Interesting, reading and writing abilities were central in the cognitive
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structure of normally developing children. Among children with epilepsy, a poor separation between modules was observed, with
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multiple domains being represented in the same module, indicating a higher degree of co-
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dependence between variable functions. Measures of attention and executive function were
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relatively peripheral to the network, possibly suggesting an immature pattern of development. Children with new onset epilepsy exhibited a significantly higher rate of academic
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problems (AP) compared to healthy controls. AP was defined in the broad sense of requiring
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ancillary educational services to address school based struggles [e.g., IEP, school based tutoring,
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summer school]). 59% of children with epilepsy exhibited AP, while 20% of controls exhibited
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AP. We did not observe a significant qualitative interaction between AP and epilepsy on the
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conformation of the cognitive networks, indicating that the diagnosis of epilepsy, whether
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concurrent or not with AP, was the most important determinant of the arrangement of the
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cognitive network conformation.
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3.2. Global network metrics
We observed a mildly reduced normalized measure of small-worldness in the cognitive network of patients, compared with controls (1.003 versus 1.004).
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Overall, global network metrics were different in patients compared with controls. These are demonstrated in Figure 3. Children with epilepsy demonstrated an increase in
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average clustering coefficient (controls=0.28± 0.04, patients=0.29± 0.03), degree
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(controls=20.6± 1.0, patients=21.4± 0.6) and efficiency (controls=0.30± 0.03, patients=0.31±
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0.02), while average betweenness centrality was reduced in patients (controls=32.6± 5.9,
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patients=31.2± 5.6). These results indicate that children with epilepsy exhibited a network less
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organized into a well-segregated structure.
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Figure 3 about here
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3.3. Local network metrics 9
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We observed a significant rearrangement of nodal participation in community structure across groups. Statistical results comparing nodal modularity co-occurrence matrices are
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demonstrated in Supplementary Figure 1. As expected from the visual demonstration of
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network structures shown in Figure 1, several tests associations were different in patients
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compared with controls. A comprehensive list of significant changes in associations is presented
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in Supplementary Table 1. Overall, it is possible to note a pattern of changes indicating that
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patients exhibited a higher association between non-verbal intelligence with other tests, while
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a relative decrement of associations between verbal skills and other modalities.
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4. Discussion
In this study, we employed graph theory to chart the structure of the cognitive network
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across multiple neuropsychological domains in children with new-onset epilepsy compared to
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normally developing children. To our knowledge, this study is the first to use graph theory
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measures to investigate the cognitive network in children with epilepsy.
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There were two main findings. First, children with epilepsy, compared to healthy controls,
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demonstrated a less well-organized network with poorly separated modules. Moreover,
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measures of attention and executive function were isolated from other modules. Second, the
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less demarcated modularity arrangement was also reflected by global network measures.
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Specifically, the cognitive networks of children with epilepsy were organized into a less well-
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segregated architecture with higher cluster coefficient, greater degree connectivity, and shorter
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path length.
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Whereas numerous studies have documented the impact of epilepsy on cognition, our
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study stands out from the traditional paediatric cognitive literature. Instead of focusing on
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general intelligence or multiple domain specific deficits, which, when simultaneously examined,
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can be confounded by multiple comparisons (i.e. type 1) errors (for a review see (Lin, Mula, &
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Hermann, 2012)), we characterized the cognitive network organization in its entirety using
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graph theory (Fair, Bathula, Nikolas, & Nigg, 2012). Using community detection methods, we
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demarcated the internal network organization and found important distinctions between
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children with epilepsy and healthy controls. Compared to controls, children with epilepsy
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demonstrated less distinct separation among the modules and greater dependency among 10
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cognitive domains. Whereas higher-level skills such as reading, arithmetic, and spelling were
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central to the modular organization of healthy controls, these academic skills were intermixed
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among other cognitive abilities in children with epilepsy. Importantly, speed and executive
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function were not well integrated and placed conspicuously peripheral in the network of
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children with epilepsy. The lack of interaction of processing speed and attention into the
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other cognitive domains in the epilepsy group might support the consistent findings in the
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literature of impaired attention and processing speed in these children (Austin et al., 2001;
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Austin et al., 2010; Helmstaedter, 2012; Semrud-Clikeman & Wical, 1999) .
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Academic skills are uniquely complex and require integration of other cognitive abilities
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including general intelligence, speed, and executive function (Rohde, 2007). Specific learning
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disorders, such as those affecting reading, writing or mathematical skills, are more frequently
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impaired in people with epilepsy than in the general population, even in children with normal
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intelligence (Sillanpaa, 1992). This poor relationship between executive function and academic
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skills exhibited in children with epilepsy provides potentially important insights into the
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underlying mechanism of poor academic performance. Indeed, attention deficit hyperactive
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disorder (ADHD) is significantly overrepresented in children at the time of diagnosis of epilepsy
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compared to controls.
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Further, patients compared to controls exhibited a higher association between non-verbal
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intelligence with other tests, while a relative decrement of associations between verbal skills
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and other modalities. The association with nonverbal tests, albeit only few of these were
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included in the study, might suggest a compensatory mechanism for the poor verbal related
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functioning.
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In summary, children with epilepsy exhibited a pattern of suboptimal network
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arrangement with greater inter-relationship among most of the cognitive domains but
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fragmented from executive function.
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Global network measures also highlighted the greater inter-reliance among cognitive
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domains in children with epilepsy. Overall, the network architecture in epilepsy exhibited
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reduced small-worldness, suggesting less optimal balance between local connectivity and global
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integration. Further, it showed greater average degree of connectivity among the cognitive
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domains and increased network segregation as indicated by augmented cluster coefficient. 11
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The altered cognitive landscape in epilepsy may represent a compensatory process, the
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underlying abnormality, or a combination of the two effects.
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5. Limitations and considerations In spite of its success, a few conceptual limitations of this study should be considered.
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Although our sample was not balanced between children with epilepsy and controls, our
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sample size in general was quite high. We also want to note that our sample did not differ
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significantly in age, education or parental education and employment status. We did not
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present specific information about those subjects experiencing academic problems (AP) as
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defined in this investigation, a factor that one might suspect to drive findings in the epilepsy
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group, being more common in the epilepsy than control group (52% vs 20%). We did inquire
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into the impact of this concept on the obtained findings and there was not a significant
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qualitative interaction between AP and epilepsy on the conformation of the cognitive networks,
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indicating that the diagnosis of epilepsy, whether concurrent or not with AP, was the most
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important determinant of the arrangement of the cognitive network.
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Finally, most existing neuropsychological tests require verbal and language skills, even if
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the test does target these domains. Our findings were predominantly related to the differential
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influence of language domains between groups. Therefore, they should be interpreted in the
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context of the neuropsychological tests that were utilized.
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6. Conclusion
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The current approach provides new insights regarding the impact of epilepsy on
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cognition and the response of the cognitive systems to epilepsy. Characterization of network
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perturbations might serve as the basis for the assessment of specific effects associated with
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epilepsy phenotypes, or to evaluate future therapies designed to mitigate the
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neuropsychological effects of epilepsy. Whereas childhood onset epilepsy influences diverse
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cognitive abilities, current therapy has focused on domain specific cognitive rehabilitation such
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as memory and attention. Overall, these treatment approaches have produced mixed results
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and why only some children improve is unclear. Understanding the inter-relationship between 12
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cognitive domains will improving our understanding of how performances in one domain may
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influence abilities in other domains. Such insights derived from this study will allow the
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development of rationale cognitive rehabilitation in individual with epilepsy and hopefully
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intervene at early in the course of epilepsy.
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Acknowledgements. The work was supported by grants from the NIH National Institute of
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Neurological Disorders and Stroke (K23 NS060993, J.J.L.; 3RO1-44351, B.P.H.). The project was
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also supported by the Clinical and Translational Science Award program, through the NIH
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National Center for Advancing Translational Sciences (grant number UL1TR000427), and the
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University of Wisconsin. We thank Raj Sheth MD, Monica Koehn MD, and Jason Dozier MD for
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study participation and subject recruitment. Also greatly appreciated are Dace Almane, Melissa
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Hanson, Kate Young, and Bjorn Hanson for overall study coordination, participant recruitment,
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cognitive assessment, and data management. Also appreciated are the contributions of Drs.
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Hsu, Stafstrom, Edelman, Jones, Jackson and Seidenberg.
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Legends for table and figures.
2 Table 1 – Group characteristics.
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Table 2 – Neuropsychological test battery employed in this study. The ordering of tests
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demonstrated here is the same as from the adjacency matrices in Figures 1 and Supplementary
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Figure 1 – Adjacency matrices demonstrating the cross-correlations between scores in
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neuropsychological tests for controls and patients. Neuropsychological tests are numbered in
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accordance with Table 2
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Figure 2 – This two-dimensional graph representation illustrates the cognitive networks
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representing the spatial relationship between cognitive tests. The spatial distribution of nodes
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was calculated using a force-atlas graph algorithm, where nodes that demonstrated stronger
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connections are located closer in space, whilst nodes with fewer connections tend to drift
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away. Nodes with a similar color belong to the same module, whereas each module is
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composed of nodes with the highest association (i.e., connectivity strength) between in-module
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nodes, and the lowest association with nodes outside the module.
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Figure 3- Box-whisker plots demonstrating global network measures. Average clustering
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coefficient, degree and efficiency were increased in patients, while average betweenness
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centrality was reduced in patients.
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Supplementary Figure 1 – The matrix on the left side of the image represents the Chi-square
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obtained by calculating whether each pair of nodes was more likely to belong to the same
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module in patients versus controls. The matrix on the right side of the image demonstrates the
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p-values associated with these Chi-square values. Note that the direction of the difference
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(patients> controls or vice-versa) is not demonstrated here, but are summarized in the
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Supplementary Table 1.
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Supplementary Table 1- Nodes that were more frequently associated within the same module
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in controls as opposed to patients, and vice-versa, are listed in this table.
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Alexander, R. P., Concha, L., Snyder, T. J., Beaulieu, C., & Gross, D. W. (2014). Correlations between Limbic White Matter and Cognitive Function in Temporal-Lobe Epilepsy, Preliminary Findings. Front Aging Neurosci, 6, 142. Austin, J. K., Harezlak, J., Dunn, D. W., Huster, G. A., Rose, D. F., & Ambrosius, W. T. (2001). Behavior problems in children before first recognized seizures. Pediatrics, 107(1), 115122. Austin, J. K., Perkins, S. M., Johnson, C. S., Fastenau, P. S., Byars, A. W., deGrauw, T. J., & Dunn, D. W. (2010). Self-esteem and symptoms of depression in children with seizures: relationships with neuropsychological functioning and family variables over time. Epilepsia, 51(10), 2074-2083. Bell, B., Lin, J. J., Seidenberg, M., & Hermann, B. (2011). The neurobiology of cognitive disorders in temporal lobe epilepsy. Nat Rev Neurol, 7(3), 154-164. Berg, A. T., Langfitt, J. T., Testa, F. M., Levy, S. R., DiMario, F., Westerveld, M., & Kulas, J. (2008). Global cognitive function in children with epilepsy: a community-based study. Epilepsia, 49(4), 608-614. Berg, A. T., Smith, S. N., Frobish, D., Levy, S. R., Testa, F. M., Beckerman, B., & Shinnar, S. (2005). Special education needs of children with newly diagnosed epilepsy. Dev Med Child Neurol, 47(11), 749-753. Blume, W. T., Luders, H. O., Mizrahi, E., Tassinari, C., van Emde Boas, W., & Engel, J., Jr. (2001). Glossary of descriptive terminology for ictal semiology: report of the ILAE task force on classification and terminology. Epilepsia, 42(9), 1212-1218. Bonilha, L., Helpern, J. A., Sainju, R., Nesland, T., Edwards, J. C., Glazier, S. S., & Tabesh, A. (2013). Presurgical connectome and postsurgical seizure control in temporal lobe epilepsy. Neurology, 81(19), 1704-1710. Bonilha, L., Nesland, T., Martz, G. U., Joseph, J. E., Spampinato, M. V., Edwards, J. C., & Tabesh, A. (2012). Medial temporal lobe epilepsy is associated with neuronal fibre loss and paradoxical increase in structural connectivity of limbic structures. J Neurol Neurosurg Psychiatry, 83(9), 903-909. Bonilha, L., Nesland, T., Rorden, C., & Fridriksson, J. (2014). Asymmetry of the structural brain connectome in healthy older adults. Front Psychiatry, 4, 186. Bonilha, L., Tabesh, A., Dabbs, K., Hsu, D. A., Stafstrom, C. E., Hermann, B. P., & Lin, J. J. (2014). Neurodevelopmental alterations of large-scale structural networks in children with newonset epilepsy. Hum Brain Mapp. doi: 10.1002/hbm.22428 Brown, J. A., Terashima, K. H., Burggren, A. C., Ercoli, L. M., Miller, K. J., Small, G. W., & Bookheimer, S. Y. (2011). Brain network local interconnectivity loss in aging APOE-4 allele carriers. Proc Natl Acad Sci U S A, 108(51), 20760-20765. Camfield, C., Breau, L., & Camfield, P. (2003). Assessing the impact of pediatric epilepsy and concomitant behavioral, cognitive, and physical/neurologic disability: Impact of Childhood Neurologic Disability Scale. Dev Med Child Neurol, 45(3), 152-159. Camfield, P., & Camfield, C. (2003). Childhood epilepsy: what is the evidence for what we think and what we do? J Child Neurol, 18(4), 272-287.
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References
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Caplan, R., Siddarth, P., Stahl, L., Lanphier, E., Vona, P., Gurbani, S., . . . Shields, W. D. (2008). Childhood absence epilepsy: behavioral, cognitive, and linguistic comorbidities. Epilepsia, 49(11), 1838-1846. Caplan, R., Siddarth, P., Vona, P., Stahl, L., Bailey, C., Gurbani, S., . . . Donald Shields, W. (2009). Language in pediatric epilepsy. Epilepsia, 50(11), 2397-2407. Conners, C. K. . (1992). Conners’ Continuous Performance Test user’s manual. Toronto, Canada: Multi-Health Systems. . D'Agati, E., Cerminara, C., Casarelli, L., Pitzianti, M., & Curatolo, P. (2012). Attention and executive functions profile in childhood absence epilepsy. Brain Dev, 34(10), 812-817. Delis, D. C., Kaplan, E., & Kramer, J. H. . (2001). Delis-Kaplan Executive Function System (D-KEFS) examiner’s manual. San Antonio, TX: The Psychological Corporation., 1-218. Dunn, L. M., & Hottel, J. V. (1961). Peabody picture vocabulary test performance of trainable mentally retarded children. Am J Ment Defic, 65, 448-452. Engel, J., Jr. (2006). Report of the ILAE classification core group. Epilepsia, 47(9), 1558-1568. Fair, D. A., Bathula, D., Nikolas, M. A., & Nigg, J. T. (2012). Distinct neuropsychological subgroups in typically developing youth inform heterogeneity in children with ADHD. Proc Natl Acad Sci U S A, 109(17), 6769-6774. Goldin, A. P., Hermida, M. J., Shalom, D. E., Elias Costa, M., Lopez-Rosenfeld, M., Segretin, M. S., . . . Sigman, M. (2014). Far transfer to language and math of a short software-based gaming intervention. Proc Natl Acad Sci U S A, 111(17), 6443-6448. Helmstaedter, C., Witt, J.A. (2012). Clinical neuropsychology in epilepsy: theoretical and practical issues. Handb Clin Neurol, 107, 437–459. Hermann, B., Jones, J., Sheth, R., Dow, C., Koehn, M., & Seidenberg, M. (2006). Children with new-onset epilepsy: neuropsychological status and brain structure. Brain, 129(Pt 10), 2609-2619. Jacomy, M., Venturini, T., Heymann, S., & Bastian, M. (2014). ForceAtlas2, a Continuous Graph Layout Algorithm for Handy Network Visualization Designed for the Gephi Software. PLoS One, 9(6), e98679. Jaeggi, S. M., Buschkuehl, M., Jonides, J., & Shah, P. (2011). Short- and long-term benefits of cognitive training. Proc Natl Acad Sci U S A, 108(25), 10081-10086. Kaplan, E., Godglass, H., Weintraub, S. . (1983). Boston Naming Test. . Philadelphia: Lea & Febiger. Lin, J. J., Mula, M., & Hermann, B. P. (2012). Uncovering the neurobehavioural comorbidities of epilepsy over the lifespan. Lancet, 380(9848), 1180-1192. Rohde, T.E. , Thompson,L. (2007). Predicting academic achievement with cognitive ability. Intelligence, 35(1), 83-92. Rubinov, M., & Sporns, O. (2010). Complex network measures of brain connectivity: uses and interpretations. Neuroimage, 52(3), 1059-1069. doi: 10.1016/j.neuroimage.2009.10.003 Seidenberg, M., Beck, N., Geisser, M., Giordani, B., Sackellares, J. C., Berent, S., . . . Boll, T. J. (1986). Academic achievement of children with epilepsy. Epilepsia, 27(6), 753-759. Semrud-Clikeman, M., & Wical, B. (1999). Components of attention in children with complex partial seizures with and without ADHD. Epilepsia, 40(2), 211-215. Sillanpaa, M. (1992). Epilepsy in children: prevalence, disability, and handicap. Epilepsia, 33(3), 444-449.
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Vaessen, M. J., Jansen, J. F., Braakman, H. M., Hofman, P. A., De Louw, A., Aldenkamp, A. P., & Backes, W. H. (2014). Functional and structural network impairment in childhood frontal lobe epilepsy. PLoS One, 9(3), e90068. Vaessen, M. J., Jansen, J. F., Vlooswijk, M. C., Hofman, P. A., Majoie, H. J., Aldenkamp, A. P., & Backes, W. H. (2012). White matter network abnormalities are associated with cognitive decline in chronic epilepsy. Cereb Cortex, 22(9), 2139-2147. van Wijk, B. C., Stam, C. J., & Daffertshofer, A. (2010). Comparing brain networks of different size and connectivity density using graph theory. PLoS One, 5(10), e13701. Wechsler, D. . (2003). Wechsler intelligence scale for children (4th ed.). San Antonio, TX: The Psychological Corporation. Williams, K. . (1997). Expressive Vocabulary Test. Circle Pines, MN: American Guidance Service. Zalesky, A., Fornito, A., Seal, M. L., Cocchi, L., Westin, C. F., Bullmore, E. T., . . . Pantelis, C. (2011). Disrupted axonal fiber connectivity in schizophrenia. Biol Psychiatry, 69(1), 8089.
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Table 1 – Characteristics of controls and epilepsy participants by subsyndrome (mean and SD); FSIQ, full-scale intelligence quotient; BECT=benign epilepsy of childhood with centrotemporal spikes; BOE=benign occipital epilepsy; TLE=temporal lobe epilepsy; CAE=childhood absence
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Patients (n=127) 12.31 (3.17) 60 101.29(13.61) 11.44 (3.25) 8.65 (3.53) BECT=31 BOE=2 TLE=12 Focal=11 CAE=10 JAE=6 JME=32 PGE/IGE NOS=12 Frontal=9 n.s.=2
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Healthy Controls (n=80) 12.69 (3.17) 39 107.65 (12.01)
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Variable Age (y, SD) Sex (number female) FSIQ (total, SD) Age of seizure onset (y, SD) Duration (months, SD) Specific Syndrome
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generalized epilepsy.
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epilepsy; JAE=juvenile absence epilepsy; JME=juvenile myoclonic epilepsy; PGE=primary
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Table 2 – Neuropsychological test battery Test Name
Cognitive ability
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
IQVOCS IQBDS IQSIMS IQMRS PPVTSTN EVTSTN READSTN SPELSTN ARITSTN IQDSYMS WLLSS WLDSS CPOMT CPCOMMT CPRTBLKT LETFLUS CATFLUS
WASI Vocabulary WASI Block Design WASI Similarities WASI Matrix Reasoning Peabody Picture Vocabulary Test Expressive Vocabulary Test WRAT-IV Reading WRAT-IV Spelling WRAT-IV Arithmetic WISC-IV Digit symbol Children’s Memory Scale-III Children’s Memory Scale-III Continuous Performance Test-II Continuous Performance Test-II Continuous Performance Test-II D-KEFS Letter Fluency D-KEFS Category Fluency
18 19 20
CATSWS COLSS WORDSS
D-KEFS D-KEFS Color-Word D-KEFS Color-Word
21
INHSS
D-KEFS Inhibition
22 23
CORSORS BNTTOT
D-KEFS Card Sorting Test Boston Naming Test
Verbal intelligence Nonverbal Intelligence Verbal intelligence Nonverbal intelligence Language (word recognition) Language (word naming) Word recognition Spelling Arithmetic calculation Speed Verbal memory Verbal memory Executive (attention) Executive function Executive function (attention) Language (lexical fluency) Language (semantic fluency) Executive function (category switching) Speed Speed Executive function (response inhibition) Executive function (problem solving) Language (naming)
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Abbreviation
AC C
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ARITSTN
and
IQVOCS
ARITSTN
and
IQMRS
ARITSTN
and
IQBDS
ARITSTN
and
READSTN
ARITSTN
and
IQSIMS
ARITSTN
and
SPELSTN
ARITSTN
and
PPVTSTN
ARITSTN
and
LETFLUS
ARITSTN
and
EVTSTN
ARITSTN
and
CORSORS
ARITSTN
and
BNTTOT
BNTTOT
and
ARITSTN
BNTTOT
and
IQBDS
BNTTOT
and
CATFLUS
BNTTOT
and
IQMRS
BNTTOT
and
CATSWS
BNTTOT
and
CORSORS
CATFLUS
and
IQVOCS
CATFLUS
and
WLLSS
CATFLUS
and
IQSIMS
CATFLUS
and
WLDSS
CATFLUS
and
PPVTSTN
CATFLUS
and
EVTSTN
CATFLUS
and
CATSWS
CATFLUS
and
BNTTOT
CATSWS
and
IQVOCS
CATSWS
CATSWS
and
IQSIMS
CATSWS
CATSWS
and
PPVTSTN
CATSWS
CATSWS
and
EVTSTN
CATSWS
CATSWS
and
CATFLUS
CATSWS
and
BNTTOT
COLSS
and
WLLSS
COLSS
and
WLDSS
COLSS
and
LETFLUS
and
COLSS
and
WORDSS
and
INHSS
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IQDSYMS
and
and
CORSORS
COLSS
and
IQDSYMS
COLSS
and
CATSWS
COLSS
and
INHSS
COLSS
and
CORSORS
IQVOCS
CORSORS
and
IQDSYMS
IQBDS
CORSORS
and
CATSWS
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and
CORSORS
and
CATSWS
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CORSORS
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Higher association in patients
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Higher association in controls
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CORSORS
and
IQSIMS
CORSORS
and
COLSS
CORSORS
and
IQMRS
CORSORS
and
WORDSS
CORSORS
and
PPVTSTN
CORSORS
and
INHSS
CORSORS
and
EVTSTN
CORSORS
and
ARITSTN
CORSORS
and
BNTTOT
CPCOMM T CPCOMM T CPCOMM
and
IQDSYMS
and
READSTN
and
CPOMT
and
SPELSTN
and
CPRTBLKT
CPCOM MT CPCOM MT CPCOM
and
LETFLUS
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and
INHSS
and
IQDSYMS
CPOMT
and
CPCOMMT
CPOMT
and
INHSS
CPRTBLK T CPRTBLK T CPRTBLK T EVTSTN
and
IQDSYMS
and
CPCOMMT
and
INHSS
and
ARITSTN
EVTSTN
and
IQBDS
EVTSTN
and
CATFLUS
EVTSTN
and
IQMRS
EVTSTN
and
CATSWS
EVTSTN
and
CORSORS
INHSS
and
CPOMT
INHSS
INHSS
and
CPCOMMT
INHSS
INHSS
and
CPRTBLKT
INHSS
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CPCOMM T CPOMT
and
CATSWS
and
COLSS
and
WORDSS
and
CORSORS
and
IQVOCS
and
IQSIMS
and
IQMRS
IQBDS
and
PPVTSTN
IQBDS
and
EVTSTN
IQBDS
and
BNTTOT
IQDSYMS
and
CATSWS
INHSS IQBDS
and
ARITSTN
IQBDS
IQBDS
and
CORSORS
IQBDS
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IQBDS
and
CPOMT
IQDSYMS
and
CPCOMMT
IQDSYMS
and
COLSS
IQDSYMS
and
CPRTBLKT
IQDSYMS
and
WORDSS
IQDSYMS
and
CORSORS
READSTN
IQMRS
and
IQVOCS
SPELSTN
IQMRS
and
IQBDS
CORSORS
IQMRS
and
IQSIMS
IQMRS
and
PPVTSTN
IQMRS
and
EVTSTN
IQMRS
and
ARITSTN
IQMRS
and
BNTTOT
IQMRS
and
IQMRS
and
AC C
and
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IQDSYMS
IQMRS
RI PT
MT
SC
T
IQSIMS
and
ARITSTN
IQSIMS
and
IQBDS
IQSIMS
and
CATFLUS
IQSIMS
and
IQMRS
IQSIMS
and
CATSWS
IQSIMS
and
CORSORS
IQVOCS
and
ARITSTN
IQVOCS
and
IQBDS
CATFLUS
IQVOCS
and
IQMRS
IQVOCS
and
CATSWS
IQVOCS
and
CORSORS
LETFLUS
and
WLLSS
LETFLUS
and
READSTN
LETFLUS
and
WLDSS
LETFLUS
and
SPELSTN
LETFLUS
and
COLSS
LETFLUS
and
ARITSTN
LETFLUS
and
WORDSS
LETFLUS
and
CPCOMMT
PPVTSTN
and
ARITSTN
PPVTSTN
and
IQBDS
PPVTSTN
and
CATFLUS
PPVTSTN
and
IQMRS
PPVTSTN
and
CATSWS
PPVTSTN
and
CORSORS
READSTN
and
IQMRS
READSTN
and
ARITSTN
READSTN
and
CPCOMMT
READSTN
and
LETFLUS
SPELSTN
and
ARITSTN
and
CPCOMMT
and
LETFLUS
and
CATFLUS
and
CATFLUS
SPELSTN
and
IQMRS
SPELSTN SPELSTN WLDSS
and
LETFLUS
WLDSS
WLDSS
and
COLSS
WLDSS
and
WORDSS
WLLSS
and
LETFLUS
WLLSS
and
COLSS
WLLSS
and
WORDSS
WORDSS
and
WLLSS
WORDSS
and
WLDSS
WORDSS
and
LETFLUS
TE D
WLLSS
WORDSS
and
IQDSYMS
WORDSS
and
CATSWS
WORDSS
and
INHSS
WORDSS
and
CORSORS
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and
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IQVOCS
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S0010-9452(15)00058-1
DOI:
10.1016/j.cortex.2015.02.001
Reference:
CORTEX 1395
To appear in:
Cortex
Received Date: 10 September 2014 Revised Date:
2 February 2015
Accepted Date: 6 February 2015
Please cite this article as: Kellermann TS, Bonilha L, Lin JJ, Hermann BP, Mapping the landscape of cognitive development in children with epilepsy, CORTEX (2015), doi: 10.1016/j.cortex.2015.02.001. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
ACCEPTED MANUSCRIPT Landscape of cognitive development in children with epilepsy
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Tanja S. Kellermann PhD1, Leonardo Bonilha MD PhD1, Jack J. Lin MD2, Bruce P. Hermann PhD3 1 Department of Neurosciences, Medical University of South Carolina, Charleston, SC; 2 Department of Neurology, University of California Irvine, Irvine, CA; 3 Department of Neurology, University of Wisconsin, Madison, WI
Manuscript word count: 3296 words Abstract word count: 248 words Figures: 3 Tables: 2
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Running head: Landscape of cognitive development in children with epilepsy
Key words: cognitive development, cognitive network, early-onset epilepsy, graph theory, neuropsychological assessment
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Disclosure: The authors report no financial or nonfinancial conflicts of interest associated with this study.
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Mapping the landscape of cognitive development in children with epilepsy
Address correspondence to:
Bruce P. Hermann PhD Matthews Neuropsychology Lab Department of Neurology, University of Wisconsin School of Medicine and Public Health 7223 UW Medical Foundation Centennial Building 1685 Highland Ave Madison, WI 53705-2281 Phone: (608) 263-5430 (Neuropsychology Lab) Fax: (608) 265-0172 email: [email protected]
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Abstract
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Objective: Normal childhood development is defined by age-dependent improvement across
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cognitive abilities, including language, memory, psychomotor speed and executive function.
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Epilepsy is often associated with a global disruption in cognitive development, however, it is
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still largely unknown how epilepsy affects the overall organization of overlapping cognitive
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domains. The aim of the study was to evaluate how childhood epilepsy affects the
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developmental interrelationships between cognitive domains.
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Methods: We performed a comprehensive assessment of neuropsychological function in 127
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children with new onset epilepsy and 80 typically developing children matched for age, gender,
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and socio-demographic status. A cross-correlation matrix between the performances across
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multiple cognitive tests was used to assess the interrelationship between cognitive modalities
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for each group (patients and controls). A weighted network composed by the cognitive domains
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as nodes, and pair-wise domain correlation as links, was assessed using graph theory analyses,
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with focus on global network structure, network hubs and community structure.
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Results: Normally developing children exhibited a cognitive network with well-defined
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modules, with verbal intelligence, reading and spelling skills occupying a central position in the
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developing network. Conversely, children with epilepsy demonstrated a less well-organized
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network with less clear separation between modules, and relative isolation of measures of
20
attention and executive function.
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Conclusion: Our findings demonstrate that childhood-onset epilepsy, even within its early
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course, is associated with an extensive disruption of cognitive neurodevelopmental
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organization. The approach used in this study may be useful to assess the effectiveness of
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future interventions aimed at mitigating the cognitive consequences of epilepsy.
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Kellermann et al.
1. Introduction Normal cognitive development is characterized by the - age dependent harmonious
3
improvement across multiple cognitive abilities. There is now considerable agreement that
4
childhood onset epilepsy disrupts this systematic developmental process with adverse impact
5
on executive function (Bell, Lin, Seidenberg, & Hermann, 2011; D'Agati, Cerminara, Casarelli,
6
Pitzianti, & Curatolo, 2012; Seidenberg et al., 1986), academic skills (Berg et al., 2005;
7
Seidenberg et al., 1986), linguistic abilities (Caplan et al., 2008; Caplan et al., 2009) and
8
emotional/behavioral quality of life (Berg et al., 2008; C. Camfield, Breau, & Camfield, 2003; P.
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Camfield & Camfield, 2003). However, cognitive domains do not operate in isolation and it
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remains undefined how epilepsy influences the interactions among different
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neuropsychological domains.
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Recent cognitive training studies support the notion that the interrelationship among domains is crucial for academic success in school-aged children. First, fluid intelligence and
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problem solving skills can be improved when children engage in working memory exercises
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(Jaeggi, Buschkuehl, Jonides, & Shah, 2011). Second, training in executive function enhances
16
other domains including language and mathematical skills (Goldin et al., 2014). Therefore, the
17
understanding of the inter-relationship among cognitive domains can provide crucial insight on
18
how childhood-onset epilepsy interferes with the global structure of cognitive development. It
19
may also aid in the identification of strategies for future targeted intervention. In this study, we employed graph theory to investigate the impact of epilepsy on the
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global cognitive landscape. Graph theory is a mathematical method that has recently been
22
applied to examine brain network structure in epilepsy, revealing global disruption in brain
23
architecture (Bonilha et al., 2013; Bonilha et al., 2012; Vaessen et al., 2014; Vaessen et al.,
24
2012). Large –scale structural morphometrical brain changes have been correlated with specific
25
cognitive deficits in epilepsy (Alexander, Concha, Snyder, Beaulieu, & Gross, 2014; Bonilha,
26
Tabesh, et al., 2014). However, to date, there has not been a comprehensive examination of
27
neuropsychological measures, as a cognitive network per se, using graph theory. To our
28
knowledge, this study is the first to use graph theory measures to investigate the cognitive
29
network in children with epilepsy.
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Kellermann et al.
We investigated the global cognitive network in a large cohort of children with newonset epilepsy and healthy controls by examining the inter-correlations of 23
3
neuropsychological measures. The neuropsychological tests were selected to provide a broad
4
profile of cognitive function, indexing abilities in six discrete cognitive domains: intelligence,
5
academic performance, language, memory, executive function, and psychomotor speed.
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We hypothesized that epilepsy may disrupt age-related cognitive development, which is manifested in altered interactions among different cognitive domains. We therefore expected
8
to find a different arrangement of the cognitive network in children with epilepsy compared
9
with healthy controls.
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2.1. Subjects
We assessed data from 127 children with epilepsy (mean age: 12.31 years; SD=3.17; girls=67, boys=60)) and 80 healthy controls (mean age: 12.69 years; SD=3.17; girls=39,
16
boys=60). There were no significant differences between the epilepsy and control families in
17
terms of parent intelligence quotient (IQ; Wechsler Abbreviated Scale of Intelligence, controls:
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109.7 ± 12.22, patients: 110.82 ± 15.41, p = 0.80), or parental employment status (full-time,
19
part-time, unemployed; mother's, chi-square = 4.72, p = 0.19; father's, chi-square = 2.68, p =
20
0.44), indicating similar socioeconomic status between the groups. The educational
21
background was also similar between patients and controls (patient mean grade= 6.2, control
22
mean grade= 6.4, p=0.69. About 53% of patients with epilepsy and 20% of controls received
23
educational services in the past. All subjects included in this study were enrolled in regular
24
schools.
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Inclusion criteria for the patient group were: 1) diagnosis of epilepsy within the past 12
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months, 2) no other neurological disorders, 3) normal neurological examinations, and 4) normal
28
clinical imaging results. A board- certified pediatric neurologist (blinded to neuropsychological
29
and interview data) confirmed that all patients had idiopathic epilepsies and provided
30
independent confirmation of syndrome diagnosis. All patients were diagnosed according to the 4
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criteria defined by the International League Against Epilepsy (ILAE) (Blume et al., 2001; Engel,
2
2006). We excluded children with intellectual disability (IQ < 70), autism, and/or other
3
neurological disorders. Specifics regarding the participant selection process have been
4
described in detail in a previous publication (Hermann et al., 2006). In general, we tried to
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stay true to the concept of “epilepsy only” as defined broadly in the literature: normal
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neurological exams, average intelligence, and attendance at regular schools.
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Healthy controls were age- and gender-comparable first-degree cousins of the epilepsy participants. All controls had no history of seizures, early initial precipitating injuries (e.g.,
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febrile convulsions), other developmental or neurologic disease, or episodes of loss of
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consciousness. Research approval was obtained from the Institutional Review Board at the
11
University of Wisconsin Medical School. Written informed consent was obtained for all subjects
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from legal guardians. Parents underwent a clinical interview and completed questionnaires to
13
characterize gestation, delivery, neurodevelopment, and seizure history. All pertinent medical
14
records were obtained after signed release of information was obtained from the parent.
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Parents were also interviewed through a structured questionnaire about their child’s school
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progress and any specific educational services provided to address academic problems (AP).
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These services included the traditional individualized educational plan (IEP) as well as early
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childhood interventions (e.g., speech, physical or occupational therapy), mandatory summer
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school, grade retention, special tutoring services, and other related services. This interview was
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conducted blinded to cognitive and behavioral results.
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Table 1 about here
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2.2. Neuropsychological Assessment A neuropsychological test battery involving 23 different cognitive tests was
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administered to all subjects, all of which are listed in Table 2. This battery assessed multiple
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domains including intelligence, academic skills, language, memory, executive function and
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cognitive/psychomotor speed. The measures listed in Table 2 came from the following tests:
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Wechsler Abbreviated Scale of Intelligence (WASI) (Wechsler, 2003), Peabody Picture
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Vocabulary Test (PPVT) (Dunn & Hottel, 1961), Expressive Vocabulary Test (EVT) (Williams, 5
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1997), Wide Range Achievement Test-IV (WRAT-IV, 2006), Wechsler Intelligence Scale for
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Children (WISC-IV) (Wechsler, 2003), Continuous Performance Task (CPT), (Conners, 1992),
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Delis-Kaplan Executive Function System (D-KEFS) (Delis, 2001), and Boston Naming Test (BNT)
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(Kaplan, 1983)).
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5 Table 2 about here 2.3. Network Analysis
In order to define the inter-relationship between cognitive domains, we assessed a network of cognitive functions defined as the correlation matrix between performances on the
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neuropsychological tests (separately for each group, i.e., patients and controls). Specifically, a
11
correlation coefficient was calculated between each possible pair of tests, resulting in 253
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possible test combinations. A correlation table was constructed based on these associations,
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representing an undirected (i.e., symmetrical along the diagonal) weighted adjacency matrix for
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each group (patients and controls).
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Prior to being submitted to the correlations, all tests were adjusted to reflect a higher score for better performance. After the adjacency matrices were constructed, only positive
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correlation coefficients were maintained to enable the visual representation of networks and
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graph theory metric calculations. Thus, the cognitive network was comprised of 23 nodes, each
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one representing each cognitive test, and weighted links between nodes representing the
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strength of positive correlation between each pair of nodes.
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2.3.1. Visual representation of network structure
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In order to appreciate the overall structure of cognitive development in each group, we
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reconstructed two-dimensional graphs based on the correlation adjacency matrices. The data
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were exported to the software Gephi (http://gephi.github.io/), where they were displayed
26
using a Force Atlas algorithm (Jacomy, Venturini, Heymann, & Bastian, 2014) (attraction
27
strength =10, repulsion strength =100, gravity=30). To improve visualization of the network
28
structure, we reconstructed the graphs based on networks containing only the links above the
29
70% percentile of weight in each group, i.e., links below the 70% percentile were given
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weight=0, while the remaining links maintained their original weights. The use of a fixed density 6
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threshold based on link weight was chosen in order to preserve the overall network
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architecture, while preserving only the strongest and more meaningful links (Bonilha, Nesland,
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Rorden, & Fridriksson, 2014; van Wijk, Stam, & Daffertshofer, 2010; Zalesky et al., 2011).
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The community structure of each network was calculated using the software Gephi and each node was coded in accordance with module participation. Nodes within a module are
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those with a higher strength of inter-relationship, with relative lower relationship with nodes
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outside the modules.
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2.3.2. Global network parameters
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Graph theory metrics were employed to evaluate global network properties. These calculations were performed using the Brain Connectivity Toolbox within the software MATLAB
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(Rubinov & Sporns, 2010). For each group, we assessed network small worldness by evaluating
13
the relation between the normalized whole-network clustering coefficient and the normalized
14
whole-network characteristic path length, as described by Brown et al (Brown et al., 2011). The
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normalized whole-network clustering coefficient was defined as the ratio between the
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clustering coefficient of the observed network and the average clustering coefficient from 100
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random networks; similarly, the normalized whole-network characteristic path length was
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defined as the ratio between the path-length of the observed network and the average path-
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length from 100 random networks.
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betweenness centrality, mean degree and mean network efficiency were calculated. In order to
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enable a quantitative assessment of each metric per group, we assessed the distribution of
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node-wise correlation by calculating an adjacency matrix from cognitive data where the values
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were resampled with replacement (i.e., bootstrapped). This was repeated 1000 times, yielding
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1000 adjacency matrices per group. For consistency, only positive entries were maintained.
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Global network metrics were assessed for each bootstrapped matrix in each group, and
differences in dispersion across groups were calculated employing t-tests.
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2.3.3. Local network parameters
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To investigate the participation of nodes within the same community structure, we calculated the network modularity using a well-defined stochastic test of modularity (Rubinov &
3
Sporns, 2010). With this approach, module classification may vary slightly between calculations
4
due to the variations in the probability distributions of local and global minima. This approach
5
confers the advantage of enabling the frequency with which nodes are classified as belonging
6
to the same module. Thus, after repeating the calculation of modularity 1000 times for each
7
group, we calculated the frequency of co-association of nodes within a module (irrespective of
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the module label). For example, 2 nodes could have been placed in the same module 90% of
9
the times in patients, but only 20% in controls. These frequencies were compared using a series
10
of Chi-squared tests to evaluate differences in link-wise associations in patients versus controls.
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For all quantitative analyses, the level of statistical significant was set of p<0.05 adjusted
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for multiple comparisons utilizing Bonferroni correction.
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3. Results
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3.1. Visual representation of network structure
The adjacency matrices representing the cross-correlations between tests are demonstrated in Figure 1.
Figure 1 about here
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The two-dimensional graphs illustrating the structure of the cognitive network for each group is
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demonstrated in Figure 2.
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Figure 2 about here
From a qualitative perspective, there were remarkable differences in network
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arrangement between groups. While controls demonstrated an organized separation between
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functional modules, patients exhibited a poorly organized pattern, with significant overlap
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between multiple modalities.
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In controls, there was a clear separation between modules composed mostly of executive function tests, verbal and arithmetic performance, memory and attention. Non8
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verbal intelligence served as the interface between verbal performance and executive function
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modules, while speed and response inhibition represented the interface between executive
3
function and verbal skills. Interesting, reading and writing abilities were central in the cognitive
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structure of normally developing children. Among children with epilepsy, a poor separation between modules was observed, with
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multiple domains being represented in the same module, indicating a higher degree of co-
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dependence between variable functions. Measures of attention and executive function were
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relatively peripheral to the network, possibly suggesting an immature pattern of development. Children with new onset epilepsy exhibited a significantly higher rate of academic
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problems (AP) compared to healthy controls. AP was defined in the broad sense of requiring
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ancillary educational services to address school based struggles [e.g., IEP, school based tutoring,
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summer school]). 59% of children with epilepsy exhibited AP, while 20% of controls exhibited
13
AP. We did not observe a significant qualitative interaction between AP and epilepsy on the
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conformation of the cognitive networks, indicating that the diagnosis of epilepsy, whether
15
concurrent or not with AP, was the most important determinant of the arrangement of the
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cognitive network conformation.
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3.2. Global network metrics
We observed a mildly reduced normalized measure of small-worldness in the cognitive network of patients, compared with controls (1.003 versus 1.004).
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Overall, global network metrics were different in patients compared with controls. These are demonstrated in Figure 3. Children with epilepsy demonstrated an increase in
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average clustering coefficient (controls=0.28± 0.04, patients=0.29± 0.03), degree
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(controls=20.6± 1.0, patients=21.4± 0.6) and efficiency (controls=0.30± 0.03, patients=0.31±
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0.02), while average betweenness centrality was reduced in patients (controls=32.6± 5.9,
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patients=31.2± 5.6). These results indicate that children with epilepsy exhibited a network less
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organized into a well-segregated structure.
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Figure 3 about here
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3.3. Local network metrics 9
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We observed a significant rearrangement of nodal participation in community structure across groups. Statistical results comparing nodal modularity co-occurrence matrices are
3
demonstrated in Supplementary Figure 1. As expected from the visual demonstration of
4
network structures shown in Figure 1, several tests associations were different in patients
5
compared with controls. A comprehensive list of significant changes in associations is presented
6
in Supplementary Table 1. Overall, it is possible to note a pattern of changes indicating that
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patients exhibited a higher association between non-verbal intelligence with other tests, while
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a relative decrement of associations between verbal skills and other modalities.
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4. Discussion
In this study, we employed graph theory to chart the structure of the cognitive network
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across multiple neuropsychological domains in children with new-onset epilepsy compared to
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normally developing children. To our knowledge, this study is the first to use graph theory
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measures to investigate the cognitive network in children with epilepsy.
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There were two main findings. First, children with epilepsy, compared to healthy controls,
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demonstrated a less well-organized network with poorly separated modules. Moreover,
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measures of attention and executive function were isolated from other modules. Second, the
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less demarcated modularity arrangement was also reflected by global network measures.
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Specifically, the cognitive networks of children with epilepsy were organized into a less well-
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segregated architecture with higher cluster coefficient, greater degree connectivity, and shorter
21
path length.
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Whereas numerous studies have documented the impact of epilepsy on cognition, our
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study stands out from the traditional paediatric cognitive literature. Instead of focusing on
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general intelligence or multiple domain specific deficits, which, when simultaneously examined,
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can be confounded by multiple comparisons (i.e. type 1) errors (for a review see (Lin, Mula, &
26
Hermann, 2012)), we characterized the cognitive network organization in its entirety using
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graph theory (Fair, Bathula, Nikolas, & Nigg, 2012). Using community detection methods, we
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demarcated the internal network organization and found important distinctions between
29
children with epilepsy and healthy controls. Compared to controls, children with epilepsy
30
demonstrated less distinct separation among the modules and greater dependency among 10
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cognitive domains. Whereas higher-level skills such as reading, arithmetic, and spelling were
2
central to the modular organization of healthy controls, these academic skills were intermixed
3
among other cognitive abilities in children with epilepsy. Importantly, speed and executive
4
function were not well integrated and placed conspicuously peripheral in the network of
5
children with epilepsy. The lack of interaction of processing speed and attention into the
6
other cognitive domains in the epilepsy group might support the consistent findings in the
7
literature of impaired attention and processing speed in these children (Austin et al., 2001;
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Austin et al., 2010; Helmstaedter, 2012; Semrud-Clikeman & Wical, 1999) .
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Academic skills are uniquely complex and require integration of other cognitive abilities
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including general intelligence, speed, and executive function (Rohde, 2007). Specific learning
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disorders, such as those affecting reading, writing or mathematical skills, are more frequently
12
impaired in people with epilepsy than in the general population, even in children with normal
13
intelligence (Sillanpaa, 1992). This poor relationship between executive function and academic
14
skills exhibited in children with epilepsy provides potentially important insights into the
15
underlying mechanism of poor academic performance. Indeed, attention deficit hyperactive
16
disorder (ADHD) is significantly overrepresented in children at the time of diagnosis of epilepsy
17
compared to controls.
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Further, patients compared to controls exhibited a higher association between non-verbal
19
intelligence with other tests, while a relative decrement of associations between verbal skills
20
and other modalities. The association with nonverbal tests, albeit only few of these were
21
included in the study, might suggest a compensatory mechanism for the poor verbal related
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functioning.
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In summary, children with epilepsy exhibited a pattern of suboptimal network
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arrangement with greater inter-relationship among most of the cognitive domains but
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fragmented from executive function.
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Global network measures also highlighted the greater inter-reliance among cognitive
27
domains in children with epilepsy. Overall, the network architecture in epilepsy exhibited
28
reduced small-worldness, suggesting less optimal balance between local connectivity and global
29
integration. Further, it showed greater average degree of connectivity among the cognitive
30
domains and increased network segregation as indicated by augmented cluster coefficient. 11
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The altered cognitive landscape in epilepsy may represent a compensatory process, the
2
underlying abnormality, or a combination of the two effects.
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5
5. Limitations and considerations In spite of its success, a few conceptual limitations of this study should be considered.
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Although our sample was not balanced between children with epilepsy and controls, our
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sample size in general was quite high. We also want to note that our sample did not differ
8
significantly in age, education or parental education and employment status. We did not
9
present specific information about those subjects experiencing academic problems (AP) as
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defined in this investigation, a factor that one might suspect to drive findings in the epilepsy
11
group, being more common in the epilepsy than control group (52% vs 20%). We did inquire
12
into the impact of this concept on the obtained findings and there was not a significant
13
qualitative interaction between AP and epilepsy on the conformation of the cognitive networks,
14
indicating that the diagnosis of epilepsy, whether concurrent or not with AP, was the most
15
important determinant of the arrangement of the cognitive network.
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Finally, most existing neuropsychological tests require verbal and language skills, even if
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the test does target these domains. Our findings were predominantly related to the differential
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influence of language domains between groups. Therefore, they should be interpreted in the
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context of the neuropsychological tests that were utilized.
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6. Conclusion
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The current approach provides new insights regarding the impact of epilepsy on
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cognition and the response of the cognitive systems to epilepsy. Characterization of network
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perturbations might serve as the basis for the assessment of specific effects associated with
26
epilepsy phenotypes, or to evaluate future therapies designed to mitigate the
27
neuropsychological effects of epilepsy. Whereas childhood onset epilepsy influences diverse
28
cognitive abilities, current therapy has focused on domain specific cognitive rehabilitation such
29
as memory and attention. Overall, these treatment approaches have produced mixed results
30
and why only some children improve is unclear. Understanding the inter-relationship between 12
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cognitive domains will improving our understanding of how performances in one domain may
2
influence abilities in other domains. Such insights derived from this study will allow the
3
development of rationale cognitive rehabilitation in individual with epilepsy and hopefully
4
intervene at early in the course of epilepsy.
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Acknowledgements. The work was supported by grants from the NIH National Institute of
7
Neurological Disorders and Stroke (K23 NS060993, J.J.L.; 3RO1-44351, B.P.H.). The project was
8
also supported by the Clinical and Translational Science Award program, through the NIH
9
National Center for Advancing Translational Sciences (grant number UL1TR000427), and the
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University of Wisconsin. We thank Raj Sheth MD, Monica Koehn MD, and Jason Dozier MD for
11
study participation and subject recruitment. Also greatly appreciated are Dace Almane, Melissa
12
Hanson, Kate Young, and Bjorn Hanson for overall study coordination, participant recruitment,
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cognitive assessment, and data management. Also appreciated are the contributions of Drs.
14
Hsu, Stafstrom, Edelman, Jones, Jackson and Seidenberg.
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Legends for table and figures.
2 Table 1 – Group characteristics.
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Table 2 – Neuropsychological test battery employed in this study. The ordering of tests
5
demonstrated here is the same as from the adjacency matrices in Figures 1 and Supplementary
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Figure 1 – Adjacency matrices demonstrating the cross-correlations between scores in
8
neuropsychological tests for controls and patients. Neuropsychological tests are numbered in
9
accordance with Table 2
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Figure 2 – This two-dimensional graph representation illustrates the cognitive networks
11
representing the spatial relationship between cognitive tests. The spatial distribution of nodes
12
was calculated using a force-atlas graph algorithm, where nodes that demonstrated stronger
13
connections are located closer in space, whilst nodes with fewer connections tend to drift
14
away. Nodes with a similar color belong to the same module, whereas each module is
15
composed of nodes with the highest association (i.e., connectivity strength) between in-module
16
nodes, and the lowest association with nodes outside the module.
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Figure 3- Box-whisker plots demonstrating global network measures. Average clustering
18
coefficient, degree and efficiency were increased in patients, while average betweenness
19
centrality was reduced in patients.
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Supplementary Figure 1 – The matrix on the left side of the image represents the Chi-square
2
obtained by calculating whether each pair of nodes was more likely to belong to the same
3
module in patients versus controls. The matrix on the right side of the image demonstrates the
4
p-values associated with these Chi-square values. Note that the direction of the difference
5
(patients> controls or vice-versa) is not demonstrated here, but are summarized in the
6
Supplementary Table 1.
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Supplementary Table 1- Nodes that were more frequently associated within the same module
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in controls as opposed to patients, and vice-versa, are listed in this table.
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Alexander, R. P., Concha, L., Snyder, T. J., Beaulieu, C., & Gross, D. W. (2014). Correlations between Limbic White Matter and Cognitive Function in Temporal-Lobe Epilepsy, Preliminary Findings. Front Aging Neurosci, 6, 142. Austin, J. K., Harezlak, J., Dunn, D. W., Huster, G. A., Rose, D. F., & Ambrosius, W. T. (2001). Behavior problems in children before first recognized seizures. Pediatrics, 107(1), 115122. Austin, J. K., Perkins, S. M., Johnson, C. S., Fastenau, P. S., Byars, A. W., deGrauw, T. J., & Dunn, D. W. (2010). Self-esteem and symptoms of depression in children with seizures: relationships with neuropsychological functioning and family variables over time. Epilepsia, 51(10), 2074-2083. Bell, B., Lin, J. J., Seidenberg, M., & Hermann, B. (2011). The neurobiology of cognitive disorders in temporal lobe epilepsy. Nat Rev Neurol, 7(3), 154-164. Berg, A. T., Langfitt, J. T., Testa, F. M., Levy, S. R., DiMario, F., Westerveld, M., & Kulas, J. (2008). Global cognitive function in children with epilepsy: a community-based study. Epilepsia, 49(4), 608-614. Berg, A. T., Smith, S. N., Frobish, D., Levy, S. R., Testa, F. M., Beckerman, B., & Shinnar, S. (2005). Special education needs of children with newly diagnosed epilepsy. Dev Med Child Neurol, 47(11), 749-753. Blume, W. T., Luders, H. O., Mizrahi, E., Tassinari, C., van Emde Boas, W., & Engel, J., Jr. (2001). Glossary of descriptive terminology for ictal semiology: report of the ILAE task force on classification and terminology. Epilepsia, 42(9), 1212-1218. Bonilha, L., Helpern, J. A., Sainju, R., Nesland, T., Edwards, J. C., Glazier, S. S., & Tabesh, A. (2013). Presurgical connectome and postsurgical seizure control in temporal lobe epilepsy. Neurology, 81(19), 1704-1710. Bonilha, L., Nesland, T., Martz, G. U., Joseph, J. E., Spampinato, M. V., Edwards, J. C., & Tabesh, A. (2012). Medial temporal lobe epilepsy is associated with neuronal fibre loss and paradoxical increase in structural connectivity of limbic structures. J Neurol Neurosurg Psychiatry, 83(9), 903-909. Bonilha, L., Nesland, T., Rorden, C., & Fridriksson, J. (2014). Asymmetry of the structural brain connectome in healthy older adults. Front Psychiatry, 4, 186. Bonilha, L., Tabesh, A., Dabbs, K., Hsu, D. A., Stafstrom, C. E., Hermann, B. P., & Lin, J. J. (2014). Neurodevelopmental alterations of large-scale structural networks in children with newonset epilepsy. Hum Brain Mapp. doi: 10.1002/hbm.22428 Brown, J. A., Terashima, K. H., Burggren, A. C., Ercoli, L. M., Miller, K. J., Small, G. W., & Bookheimer, S. Y. (2011). Brain network local interconnectivity loss in aging APOE-4 allele carriers. Proc Natl Acad Sci U S A, 108(51), 20760-20765. Camfield, C., Breau, L., & Camfield, P. (2003). Assessing the impact of pediatric epilepsy and concomitant behavioral, cognitive, and physical/neurologic disability: Impact of Childhood Neurologic Disability Scale. Dev Med Child Neurol, 45(3), 152-159. Camfield, P., & Camfield, C. (2003). Childhood epilepsy: what is the evidence for what we think and what we do? J Child Neurol, 18(4), 272-287.
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References
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1
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Caplan, R., Siddarth, P., Stahl, L., Lanphier, E., Vona, P., Gurbani, S., . . . Shields, W. D. (2008). Childhood absence epilepsy: behavioral, cognitive, and linguistic comorbidities. Epilepsia, 49(11), 1838-1846. Caplan, R., Siddarth, P., Vona, P., Stahl, L., Bailey, C., Gurbani, S., . . . Donald Shields, W. (2009). Language in pediatric epilepsy. Epilepsia, 50(11), 2397-2407. Conners, C. K. . (1992). Conners’ Continuous Performance Test user’s manual. Toronto, Canada: Multi-Health Systems. . D'Agati, E., Cerminara, C., Casarelli, L., Pitzianti, M., & Curatolo, P. (2012). Attention and executive functions profile in childhood absence epilepsy. Brain Dev, 34(10), 812-817. Delis, D. C., Kaplan, E., & Kramer, J. H. . (2001). Delis-Kaplan Executive Function System (D-KEFS) examiner’s manual. San Antonio, TX: The Psychological Corporation., 1-218. Dunn, L. M., & Hottel, J. V. (1961). Peabody picture vocabulary test performance of trainable mentally retarded children. Am J Ment Defic, 65, 448-452. Engel, J., Jr. (2006). Report of the ILAE classification core group. Epilepsia, 47(9), 1558-1568. Fair, D. A., Bathula, D., Nikolas, M. A., & Nigg, J. T. (2012). Distinct neuropsychological subgroups in typically developing youth inform heterogeneity in children with ADHD. Proc Natl Acad Sci U S A, 109(17), 6769-6774. Goldin, A. P., Hermida, M. J., Shalom, D. E., Elias Costa, M., Lopez-Rosenfeld, M., Segretin, M. S., . . . Sigman, M. (2014). Far transfer to language and math of a short software-based gaming intervention. Proc Natl Acad Sci U S A, 111(17), 6443-6448. Helmstaedter, C., Witt, J.A. (2012). Clinical neuropsychology in epilepsy: theoretical and practical issues. Handb Clin Neurol, 107, 437–459. Hermann, B., Jones, J., Sheth, R., Dow, C., Koehn, M., & Seidenberg, M. (2006). Children with new-onset epilepsy: neuropsychological status and brain structure. Brain, 129(Pt 10), 2609-2619. Jacomy, M., Venturini, T., Heymann, S., & Bastian, M. (2014). ForceAtlas2, a Continuous Graph Layout Algorithm for Handy Network Visualization Designed for the Gephi Software. PLoS One, 9(6), e98679. Jaeggi, S. M., Buschkuehl, M., Jonides, J., & Shah, P. (2011). Short- and long-term benefits of cognitive training. Proc Natl Acad Sci U S A, 108(25), 10081-10086. Kaplan, E., Godglass, H., Weintraub, S. . (1983). Boston Naming Test. . Philadelphia: Lea & Febiger. Lin, J. J., Mula, M., & Hermann, B. P. (2012). Uncovering the neurobehavioural comorbidities of epilepsy over the lifespan. Lancet, 380(9848), 1180-1192. Rohde, T.E. , Thompson,L. (2007). Predicting academic achievement with cognitive ability. Intelligence, 35(1), 83-92. Rubinov, M., & Sporns, O. (2010). Complex network measures of brain connectivity: uses and interpretations. Neuroimage, 52(3), 1059-1069. doi: 10.1016/j.neuroimage.2009.10.003 Seidenberg, M., Beck, N., Geisser, M., Giordani, B., Sackellares, J. C., Berent, S., . . . Boll, T. J. (1986). Academic achievement of children with epilepsy. Epilepsia, 27(6), 753-759. Semrud-Clikeman, M., & Wical, B. (1999). Components of attention in children with complex partial seizures with and without ADHD. Epilepsia, 40(2), 211-215. Sillanpaa, M. (1992). Epilepsy in children: prevalence, disability, and handicap. Epilepsia, 33(3), 444-449.
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Vaessen, M. J., Jansen, J. F., Braakman, H. M., Hofman, P. A., De Louw, A., Aldenkamp, A. P., & Backes, W. H. (2014). Functional and structural network impairment in childhood frontal lobe epilepsy. PLoS One, 9(3), e90068. Vaessen, M. J., Jansen, J. F., Vlooswijk, M. C., Hofman, P. A., Majoie, H. J., Aldenkamp, A. P., & Backes, W. H. (2012). White matter network abnormalities are associated with cognitive decline in chronic epilepsy. Cereb Cortex, 22(9), 2139-2147. van Wijk, B. C., Stam, C. J., & Daffertshofer, A. (2010). Comparing brain networks of different size and connectivity density using graph theory. PLoS One, 5(10), e13701. Wechsler, D. . (2003). Wechsler intelligence scale for children (4th ed.). San Antonio, TX: The Psychological Corporation. Williams, K. . (1997). Expressive Vocabulary Test. Circle Pines, MN: American Guidance Service. Zalesky, A., Fornito, A., Seal, M. L., Cocchi, L., Westin, C. F., Bullmore, E. T., . . . Pantelis, C. (2011). Disrupted axonal fiber connectivity in schizophrenia. Biol Psychiatry, 69(1), 8089.
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Kellermann et al.
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Table 1 – Characteristics of controls and epilepsy participants by subsyndrome (mean and SD); FSIQ, full-scale intelligence quotient; BECT=benign epilepsy of childhood with centrotemporal spikes; BOE=benign occipital epilepsy; TLE=temporal lobe epilepsy; CAE=childhood absence
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Patients (n=127) 12.31 (3.17) 60 101.29(13.61) 11.44 (3.25) 8.65 (3.53) BECT=31 BOE=2 TLE=12 Focal=11 CAE=10 JAE=6 JME=32 PGE/IGE NOS=12 Frontal=9 n.s.=2
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Healthy Controls (n=80) 12.69 (3.17) 39 107.65 (12.01)
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Variable Age (y, SD) Sex (number female) FSIQ (total, SD) Age of seizure onset (y, SD) Duration (months, SD) Specific Syndrome
SC
generalized epilepsy.
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epilepsy; JAE=juvenile absence epilepsy; JME=juvenile myoclonic epilepsy; PGE=primary
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Table 2 – Neuropsychological test battery Test Name
Cognitive ability
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
IQVOCS IQBDS IQSIMS IQMRS PPVTSTN EVTSTN READSTN SPELSTN ARITSTN IQDSYMS WLLSS WLDSS CPOMT CPCOMMT CPRTBLKT LETFLUS CATFLUS
WASI Vocabulary WASI Block Design WASI Similarities WASI Matrix Reasoning Peabody Picture Vocabulary Test Expressive Vocabulary Test WRAT-IV Reading WRAT-IV Spelling WRAT-IV Arithmetic WISC-IV Digit symbol Children’s Memory Scale-III Children’s Memory Scale-III Continuous Performance Test-II Continuous Performance Test-II Continuous Performance Test-II D-KEFS Letter Fluency D-KEFS Category Fluency
18 19 20
CATSWS COLSS WORDSS
D-KEFS D-KEFS Color-Word D-KEFS Color-Word
21
INHSS
D-KEFS Inhibition
22 23
CORSORS BNTTOT
D-KEFS Card Sorting Test Boston Naming Test
Verbal intelligence Nonverbal Intelligence Verbal intelligence Nonverbal intelligence Language (word recognition) Language (word naming) Word recognition Spelling Arithmetic calculation Speed Verbal memory Verbal memory Executive (attention) Executive function Executive function (attention) Language (lexical fluency) Language (semantic fluency) Executive function (category switching) Speed Speed Executive function (response inhibition) Executive function (problem solving) Language (naming)
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ARITSTN
and
IQVOCS
ARITSTN
and
IQMRS
ARITSTN
and
IQBDS
ARITSTN
and
READSTN
ARITSTN
and
IQSIMS
ARITSTN
and
SPELSTN
ARITSTN
and
PPVTSTN
ARITSTN
and
LETFLUS
ARITSTN
and
EVTSTN
ARITSTN
and
CORSORS
ARITSTN
and
BNTTOT
BNTTOT
and
ARITSTN
BNTTOT
and
IQBDS
BNTTOT
and
CATFLUS
BNTTOT
and
IQMRS
BNTTOT
and
CATSWS
BNTTOT
and
CORSORS
CATFLUS
and
IQVOCS
CATFLUS
and
WLLSS
CATFLUS
and
IQSIMS
CATFLUS
and
WLDSS
CATFLUS
and
PPVTSTN
CATFLUS
and
EVTSTN
CATFLUS
and
CATSWS
CATFLUS
and
BNTTOT
CATSWS
and
IQVOCS
CATSWS
CATSWS
and
IQSIMS
CATSWS
CATSWS
and
PPVTSTN
CATSWS
CATSWS
and
EVTSTN
CATSWS
CATSWS
and
CATFLUS
CATSWS
and
BNTTOT
COLSS
and
WLLSS
COLSS
and
WLDSS
COLSS
and
LETFLUS
and
COLSS
and
WORDSS
and
INHSS
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IQDSYMS
and
and
CORSORS
COLSS
and
IQDSYMS
COLSS
and
CATSWS
COLSS
and
INHSS
COLSS
and
CORSORS
IQVOCS
CORSORS
and
IQDSYMS
IQBDS
CORSORS
and
CATSWS
EP
and
CORSORS
and
CATSWS
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CORSORS
SC
Higher association in patients
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CORSORS
and
IQSIMS
CORSORS
and
COLSS
CORSORS
and
IQMRS
CORSORS
and
WORDSS
CORSORS
and
PPVTSTN
CORSORS
and
INHSS
CORSORS
and
EVTSTN
CORSORS
and
ARITSTN
CORSORS
and
BNTTOT
CPCOMM T CPCOMM T CPCOMM
and
IQDSYMS
and
READSTN
and
CPOMT
and
SPELSTN
and
CPRTBLKT
CPCOM MT CPCOM MT CPCOM
and
LETFLUS
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and
INHSS
and
IQDSYMS
CPOMT
and
CPCOMMT
CPOMT
and
INHSS
CPRTBLK T CPRTBLK T CPRTBLK T EVTSTN
and
IQDSYMS
and
CPCOMMT
and
INHSS
and
ARITSTN
EVTSTN
and
IQBDS
EVTSTN
and
CATFLUS
EVTSTN
and
IQMRS
EVTSTN
and
CATSWS
EVTSTN
and
CORSORS
INHSS
and
CPOMT
INHSS
INHSS
and
CPCOMMT
INHSS
INHSS
and
CPRTBLKT
INHSS
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CPCOMM T CPOMT
and
CATSWS
and
COLSS
and
WORDSS
and
CORSORS
and
IQVOCS
and
IQSIMS
and
IQMRS
IQBDS
and
PPVTSTN
IQBDS
and
EVTSTN
IQBDS
and
BNTTOT
IQDSYMS
and
CATSWS
INHSS IQBDS
and
ARITSTN
IQBDS
IQBDS
and
CORSORS
IQBDS
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IQBDS
and
CPOMT
IQDSYMS
and
CPCOMMT
IQDSYMS
and
COLSS
IQDSYMS
and
CPRTBLKT
IQDSYMS
and
WORDSS
IQDSYMS
and
CORSORS
READSTN
IQMRS
and
IQVOCS
SPELSTN
IQMRS
and
IQBDS
CORSORS
IQMRS
and
IQSIMS
IQMRS
and
PPVTSTN
IQMRS
and
EVTSTN
IQMRS
and
ARITSTN
IQMRS
and
BNTTOT
IQMRS
and
IQMRS
and
AC C
and
EP
IQDSYMS
IQMRS
RI PT
MT
SC
T
IQSIMS
and
ARITSTN
IQSIMS
and
IQBDS
IQSIMS
and
CATFLUS
IQSIMS
and
IQMRS
IQSIMS
and
CATSWS
IQSIMS
and
CORSORS
IQVOCS
and
ARITSTN
IQVOCS
and
IQBDS
CATFLUS
IQVOCS
and
IQMRS
IQVOCS
and
CATSWS
IQVOCS
and
CORSORS
LETFLUS
and
WLLSS
LETFLUS
and
READSTN
LETFLUS
and
WLDSS
LETFLUS
and
SPELSTN
LETFLUS
and
COLSS
LETFLUS
and
ARITSTN
LETFLUS
and
WORDSS
LETFLUS
and
CPCOMMT
PPVTSTN
and
ARITSTN
PPVTSTN
and
IQBDS
PPVTSTN
and
CATFLUS
PPVTSTN
and
IQMRS
PPVTSTN
and
CATSWS
PPVTSTN
and
CORSORS
READSTN
and
IQMRS
READSTN
and
ARITSTN
READSTN
and
CPCOMMT
READSTN
and
LETFLUS
SPELSTN
and
ARITSTN
and
CPCOMMT
and
LETFLUS
and
CATFLUS
and
CATFLUS
SPELSTN
and
IQMRS
SPELSTN SPELSTN WLDSS
and
LETFLUS
WLDSS
WLDSS
and
COLSS
WLDSS
and
WORDSS
WLLSS
and
LETFLUS
WLLSS
and
COLSS
WLLSS
and
WORDSS
WORDSS
and
WLLSS
WORDSS
and
WLDSS
WORDSS
and
LETFLUS
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WLLSS
WORDSS
and
IQDSYMS
WORDSS
and
CATSWS
WORDSS
and
INHSS
WORDSS
and
CORSORS
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