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Hum Brain Mapp. Author manuscript; available in PMC 2017 November 01. Published in final edited form as: Hum Brain Mapp. 2016 November ; 37(11): 3946–3956. doi:10.1002/hbm.23287.
Post-operative axonal changes in the contralateral hemisphere in children with medically refractory epilepsy: a longitudinal DTI connectome analysis Jeong-Won Jeong1,2, Eishi Asano1,2, Csaba Juhász1,2, Michael E. Behen1,2, and Harry T. Chugani3,4
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1Departments
of Pediatrics and Neurology, School of Medicine, Wayne State University, Detroit,
MI, USA 2Translational 3Department 4Thomas
Imaging Laboratory, PET Center, Children’s Hospital of Michigan, Detroit, MI, USA
of Neurology, Nemours DuPont Hospital for Children, Wilmington, DE, USA
Jefferson University School of Medicine, Philadelphia, PA, USA
Abstract
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To determine brain plasticity changes due to resective epilepsy surgery in children, we performed a longitudinal connectome analysis on the pattern of axonal connectivity in the contralateral hemisphere. Pre- and post-operative diffusion tensor imaging (DTI) data were acquired from 35 children with intractable focal epilepsy. A total of 54 brain regions of interest (ROIs) were generated in the hemisphere contralateral to the resection. Within a 54×54 connectivity matrix, a pair-wise connectivity score was calculated for each connection between two ROIs, based on the DTI fiber streamline number in each connection. A permuted Spearman’s ρ-rank analysis was used to identify specific inter-regional connections showing a significant association between the post-operative change of connectivity score and clinical variables. Nineteen connections in the contralateral hemisphere showed postoperative increases in the strength of connectivity. Postoperative increase in connectivity between insular-inferior frontal operculum regions as well as that between superior frontal orbital-mid frontal orbital regions were both significantly associated with a larger surgical resection volume (ρ>+0.40) and a younger patient age (ρ>−0.34). These increases were more robust in patients with frontal resection and in those achieving seizure freedom. Neuropsychological evaluation on subsets of patients revealed that such increases in connectivity were associated with preserved or improved cognitive functions such as visual memory and planning. Resective epilepsy surgery may lead to increased contralateral axonal connectivity in children with focal epilepsy. Our data lead to a hypothesis that such increased connectivity may be an imaging marker of post-operative brain plasticity to compensate for cognitive function.
Corresponding author: Jeong-Won Jeong, PhD, Departments of Pediatrics and Neurology, Wayne State University School of Medicine, PET Center, Children’s Hospital of Michigan, 3901 Beaubien St., Detroit, MI, 48201, Phone: 313-993-0258; Fax: 313-966-9228; [email protected]. The authors declare no conflicts of interest.
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Keywords Diffusion tensor imaging; Epilepsy surgery; Axonal plasticity; Contralateral reorganization
Introduction
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Medically refractory epilepsy may lead to progressive deterioration of the function of affected brain structures concurrent with compensatory changes (i.e., functional reorganization) in non-epileptic areas [Lespinet et al., 2002; Hermann et al., 2002]. However, persistent focal epilepsy early in development can interfere with the brain’s potential for functional reorganization [Chugani et al., 1996]. In addition, ictal and interictal epileptiform activities can modify neural networks, resulting in neuro-cognitive and behavioral disturbances [Benuzzi et al., 2004]. Indeed, emerging data suggest that repeated seizures may reconfigure long-range connections between neuronal populations in different parts of the brain [Bonilha et al., 2012; Bonilha et al., 2013; DeSalvo et al., 2014]. The fact that some children with intractable epilepsy undergo large cortical resections provides a unique opportunity to evaluate post-operative reorganizational changes of white matter as well as the clinical correlates of such reorganization in these children.
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Previous studies using functional MRI (fMRI) have reported functional changes before and after epilepsy surgery, mainly focusing on cognition including language and memory functions [Billingsley et al., 2001; Pataraia et al., 2005; Backes et al., 2005; Maccotta et al., 2007; Wong et al., 2009]. Recently, it was also demonstrated that anterior temporal lobectomy elicited alterations allowing behavioral and hemodynamic improvement in emotion recognition [Benuzzi et al., 2014], suggesting that successful temporal lobectomy may activate the process of functional plasticity in early-onset medial temporal lobe epilepsy (TLE). These studies consistently supported the notion that post-operative functional reorganization may involve both intra- and inter-hemispheric reorganization of specific areas.
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A recent study using diffusion tensor imaging (DTI) showed that the axonal integrity of temporal lobe-related fiber tracts was decreased for a period of >1 year after surgery in the ipsilateral hemisphere of TLE adult patients, indicating Wallerian degeneration of the affected fiber bundles following surgical disconnection of neurons [Faber et al., 2013]. However, whether surgical intervention affects the trajectory of axonal reorganization in the contralateral hemisphere of children with TLE or extra-TLE remains unknown. This is of particular relevance since the integrity of the contralateral hemisphere is important in impacting post-surgical neuro-cognitive outcome [Althausen et al., 2013]. The aim of the present study was to investigate the potential effect of resective epilepsy surgery on the pattern of axonal connectivity in the contralateral hemisphere in children with intractable focal epilepsy. Toward this goal, we applied a state-of-the-art whole brain connectome analysis method. Connectome analysis is a recently developed method using structural and functional neuroimaging techniques including DTI and fMRI, focused on quantifying and analyzing properties of complex large-scale connectivity of brain networks. The present study views epilepsy as a network disease and models the epileptic brain as a Hum Brain Mapp. Author manuscript; available in PMC 2017 November 01.
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network or a graph using a connectome represented by a collection of nodes (i.e., cortical and subcortical regions) and links (i.e., axonal fiber counts between nodes) in order to examine the post-operative changes in nodal links in the contralateral (non-operated) hemisphere. The analyses performed in this study were driven by the hypothesis that surgical intervention may differentially drive axonal reorganization of the contralateral hemisphere depending on age as well as volume and location of surgical resection. We hypothesized that: 1) larger surgical resections will be associated with more altered, possibly increased, nodal links in specific regions of the contralateral hemisphere indicating a distinctive pattern of axonal plasticity after surgery, and 2) such changes will be associated with post-operative changes in specific cognitive domains. We were particularly interested in the effect of resection of the frontal lobe, the largest among the four lobes in the human brain.
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Methods Subjects Thirty-five children with epilepsy who underwent epilepsy surgery between 2008 and 2014 at the Children’s Hospital of Michigan (Detroit) were retrospectively selected for the study (mean age ± standard deviation [SD]: 8.7±4.9 years, 19 boys). Resections included the frontal lobe in 16 children and were extra-frontal (temporal, parietal, or/and occipital) in 19 (Table 1). The study excluded surgical patients who had severe, extensive bilateral lesions such as tuberous sclerosis complex, which might affect, regardless of resective surgery, the tracking of white matter terminals in connectome analysis in the contralateral hemisphere.
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The hemisphere contralateral to the resection was normal on MRI in all children. Twenty children had surgery in the left hemisphere and 15 children in the right hemisphere. The surgical outcome was assessed at least 1 year after surgery (time of post-surgical follow-up= 3.2±1.5 years), categorized into seizure-free (n=24) and not seizure-free groups (n=11). The two groups did not differ for duration of epilepsy (4.7±3.0 and 4.7±4.5 years; p-value=0.99), age at onset of epilepsy (4.2±3.9 and 2.9±3.6 years; p-value=0.32), or resection volume (50.6±50.1 and 57.8±50.9 ×1000mm3, p-value=0.62).
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Thirty-six children with no history of medical, psychiatric disease and normal cognitive function, defined by measured global cognition, language, and adaptive behavior (communication, daily living, socialization, motor) skills within normal limits (standard score > 85), were recruited as healthy controls (age: 9.1±5.2 years, 21 boys). The two groups (control, epilepsy) did not differ by age (p-value= 0.52) or gender distribution (p-value= 0.77). The study was approved by Wayne State University’s Institutional Review Board and written informed consent was obtained from all parents/guardians. Neuropsychological assessment All children underwent comprehensive neurocognitive evaluations, including assessment of verbal and nonverbal intellectual functioning (using the age appropriate Wechsler measures, WPPSI-3 for children 6 years of age or younger; WISC-IV for children 6 years of age and older); receptive and expressive language processing (using the appropriate Comprehensive
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Evaluation of Language Fundamentals, CELF-Preschool for children 5 years, expressive and receptive language indices); executive functions (in children 5 years of age and up, including sustained attention and behavioral control; Gordon Diagnostic Scale Delay and Vigilance Tasks), verbal and visual memory (in children 5 years and up, Wide Range Assessment of Memory and Learning-2nd Edition), and manual dexterity for both the dominant and non-dominant hands (Grooved Pegs Test for children 5 years and up, or Purdue Pegboard for children younger than 5 years). All of the above measures have established psychometric properties and are widely used in clinical and research samples. All assessments were completed within two weeks of the MRI scans. MRI acquisition
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All MRI scans were performed at the Children’s Hospital of Michigan on a 3T GE-Signa scanner (GE Healthcare, Milwaukee, WI) equipped with an 8-channel head coil and ASSET. DTI was acquired with a multi-slice single shot diffusion weighted echo-planar-imaging sequence at TR = 12,500ms, TE = 88.7ms, FOV = 24cm, 128×128 acquisition matrix (nominal resolution = 1.89mm), contiguous 3mm thickness in order to cover entire axial slices of whole brain using 55 isotropic gradient directions with b= 1000s/mm2, one b=0 acquisition, and number of excitations = 1. For anatomical reference, a three-dimensional fast spoiled gradient echo sequence (FSPGR) was acquired for each participant at TR/TE/TI of 9.12/3.66/400 ms, slice thickness of 1.2 mm, and planar resolution of 0.94×0.94 mm2. Because the scans for children with epilepsy surgery were clinical MRI studies, sedation was used as necessary. Patients had pre-operative MRI 3.1 months (SD: 2.9) prior to surgery and post-operative MRI 8.4 months after surgery (SD: 6.2).
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None of the control children were sedated for the MRI. They were scanned while sleeping, and monitored for movement during scan. For each of 36 controls, a, b = 0 image was selected as a target image for co-registering the 55 b = 1000 images. Six motion parameters including three translation and three rotation parameters in x,y,z were estimated for each b = 1000 image using SPM 8 co-registration toolbox (www.fil.ion.ucl.ac.uk/spm). For each parameter, the absolute displacement between adjacent images was averaged to assess the degree of head motion [Ling et al., 2012]. The average value of three average translations was finally threshold at no greater than 3 mm to be considered as no significant head motion. Thirty-three of 36 controls were considered to have no significant movements and utilized for the statistical comparison with epilepsy groups. MRI analysis
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Using a drawing tool in MRIcro software (www.mricro.com), one investigator (J.J) manually demarcated the volume of surgically removed tissue on the post-surgical FSPGR image in order to estimate the resection volume from individual patients. For DTI of each subject, an independent component analysis with ball and stick model (ICA+BSM) tractography [Jeong et al., 2013] was applied for whole brain tractography. This technique was designed to minimize the intra-voxel crossing fiber problem and isolate the orientations of up to three crossing fiber bundles at every voxel. Before performing ICA+BSM tractography analysis for the structural connectivity, the NIH TORTOISE DIFF_PREP software package (https://science.nichd.nih.gov/confluence/display/nihpd/TORTOISE) was
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used to correct motion and eddy current distortion in the DTI data. Whole brain streamline tractography was then performed using ICA+BSM to reconstruct up to three crossing streamlines at every voxel of fractional anisotropy > 0.20. At every voxel, the first eigenvector of the resulting stick components having a fractional ratio > 0.05 was considered as the reconstructed fiber orientations and then utilized for the streamline tractography at step size = 0.2 voxel width, turning angle threshold = 60°, and maximal length = 250 mm.
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Using SPM DARTEL procedure [Ashburner, 2007], three age-specific T1 templates (≤ 5years, 6–10 years, ≥ 11 years) were created from T1 images of healthy controls. To parcellate brain regions of interest in each age-specific T1 template, the present study utilized an SPM automated anatomical labeling atlas (AAL, http://www.gin.cnrs.fr/spip.php) consisting of 116 regional parcellations in MNI template space. This MNI template was spatially normalized to each of three T1 templates using linear normalization, diffeomorphic normalization and iterative averaging [Akiyama et al., 2013]. The resulting age-specific AAL template was deformed into a T1 image of individual age-matched patient using SPM 12 DARTEL deformation toolbox (www.fil.ion.ucl.ac.uk). The deformed AAL template in T1 image was finally co-registered using SPM 12 to a b0 image in order to generate a total of 54 regions of interest (ROIs) in the contralateral non-operated hemisphere. Every pair of the resulting cortical regions was applied to whole brain tractography in order to sort specific streamlines connecting a pair of two regions, resulting in a 54×54 connectivity matrix in which the elements quantified the pair-wise connectivity scores, based on the fiber streamline number in each connection normalized by total volume of the applied two ROIs. The anatomical descriptions and the corresponding labels of 54 ROIs are presented in supplementary table 1 and also available in previous studies [Tzourio-Mazoyer et al., 2002, Diebrichesn J, 2006].
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Statistical analysis
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A two-way mixed ANOVA with between-subject ‘random’ factor (frontal vs. extra-frontal resection) and within-subject ‘fixed’ factor (pre- vs. post-operative) was performed for the connectivity score of each element in order to identify specific elements showing significant group-by-time interaction. Then, a permuted Spearman’s ρ-rank analysis was used to identify specific elements showing a significant association between the post-operative change of connectivity score and the resected tissue volume. For each connectivity element, we permuted the order of individual patient data: (1) post-operative score change (i.e.: postoperative score – pre-operative score) and (2) resection volume, picked 50% of total data randomly in the framework of bootstrapping analysis, and calculated Spearman’s ρ-rank correlation coefficients of post-operative score change with two regressors (resection volume and age) to identify the nodes of elements showing significant resection-age-related connectivity changes. We repeated 1000 trials for each of different subgroups (i.e.: whole group, frontal resection, extra-frontal resection, seizure-free and not seizure-free). The ρcoefficients and p-values of whole group were averaged to find out the nodes of elements showing significant correlation at p-value < 0.05. Subsequently, the post-operative connectivity score changes of the resulting nodes were correlated with post-operative neuropsychological assessment scores to investigate their functional relevance to neurocognitive outcomes.
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Results Surgical resections were tailored to include both visible individual pathology (if present) and epileptogenic regions as defined by intracranial EEG; thus the resections were highly variable across patients [Asano et al., 2009]. The highest overlap of tissue removal across all individuals was located in frontal and temporal lobes in either hemisphere. Resection volume did not differ by the side of surgery (mean volume: 49.9/70.8 ×1000mm3 for left/ right surgery; p-value = 0.10). Age at surgery did not correlate with total resection volume (Spearman’s ρ = −0.21, p-value = 0.22).
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In the comparison between pre- and post-operative connectivity score, one-way ANOVA corrected by age found that post-operative score significantly increased in 29 interregional connections widely distributed in whole brain (supplementary Figure 1). In contrast, nineteen inter-regional connections showed a significant interaction of group (frontal resection vs. extra-frontal resection) with time (pre-operative vs. post-operative) obtained from the two-way mixed ANOVA at p-value < 0.05 (Figure 1). The strength of connectivity in fifteen connections was larger in patients who underwent frontal lobe resection, whereas the strength in four connections was larger in those who underwent extra-frontal lobe resection. Compared with extra-frontal resection, frontal resection showed more widely spread nodes with significantly increased post-operative connectivity score.
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Among those 19 connections, two interregional connections, insular-inferior frontal operculum and superior frontal orbital-mid frontal orbital showed a statistically significant association between post-operative connectivity score change and corresponding volume of tissue resection (Figure 2). The post-operative connectivity score changes of these connections were positively correlated with the resection volumes (i.e., larger resection associated with more increased connectivity, ρ=0.40/0.41, p-value =0.02/0.02 for insular– inferior frontal operculum/superior frontal orbital-mid frontal orbital, Table 2). These changes were also negatively correlated with age (i.e., younger age associated with more increased connectivity, ρ=−0.34/−0.34, p-value =0.05/0.05 for insular-inferior frontal operculum/superior frontal orbital-mid frontal orbital), and were especially robust in younger children (age < 10 years) (see Figure 2 and Table 2). Also, more robust correlations were observed in children with frontal resection compared with extra-frontal resection (Table 2). In addition, the significant positive correlations between post-operative connectivity score changes and the resection volumes were present in the seizure-free group (i.e., ρ=0.59/0.48, p-value =0.00/0.03 for insular-inferior frontal operculum/superior frontal orbital-mid frontal orbital) but was not significant in patients who had post-operative seizures (Table 2). No significant negative correlations were found between the postoperative connectivity changes and the resection volumes. The connectivity scores of these two connections were measured within normal limits before surgery and increased after surgery, as indicated by the significantly higher interregional streamlines compared with healthy controls (Figure 3). Compared with healthy controls, significant increases in interregional streamlines of post-operative subgroups were found in both interregional connections (i.e., univariate post-hoc p-value corrected using Bonferroni for multiple comparisons = 0.03/0.009 for insular–inferior frontal operculum/superior frontal
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orbital-mid frontal orbital, respectively). In subgroup analysis of age 5–10 year old using one-way ANOVA with covariate of age, post-operative connectivity score was significantly increased compared with pre-operative and healthy control (i.e., p-value= 0.03/0.05 and 0.04/0.05 for insular–inferior frontal operculum/superior frontal orbital-mid frontal orbital, respectively). There was no correlation between the time of post-operative DTI time and the change in post-operative connectivity score in the whole seizure group (Figure 4, ρ-coefficient/pvalue=−0.07/0.54 and 0.02/0.88 for insular-Frontal Inf Oper and Frontal Sup Orb-Frontal Mid Orb, respectively). The most increases were observed between 5 and 12 months of postoperative DTI.
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In a subgroup of patients where both pre- and post-operative neuro-cognitive scores were available (n=7, index: 4,9,11,13,19,22, 35 in Table 1), post-operative changes in two cognitive domains, visual memory and Gordon Diagnostic Scale Delay score (planning/ organization skills), were positively correlated with post-operative connectivity increases in insular-inferior frontal operculum and superior frontal orbital-mid frontal orbital at pvalue=0.03 and 0.01, respectively (i.e., increased connectivity related to better postoperative function, Figure 5). Note that two seizure-free children (index: 13,35) showed apparently increased connectivity scores associated with improved cognitive functions.
Discussion
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The present study demonstrates the potential use of whole brain connectome analysis as a new tool to assess short-term post-operative plasticity in the contralateral hemisphere of children with drug-resistant focal epilepsy. In group comparisons, we found increased postoperative connectivity in a number of connections in the contralateral hemisphere, which differed according to the location of resections (frontal vs. extra-frontal, see Figure 1). Among these, two connections in insular and frontal orbital cortex demonstrated postoperative increases associated with a larger volume of surgical resection and younger age as well as seizure-free outcome. Although preliminary, the results also reveal an association between increased connectivity and preserved or improved cognitive functions in specific domains. Thus, postoperative changes in DTI-detected connectivity may be an effective imaging marker of functionally relevant structural brain plasticity associated with surgical intervention.
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Alterations in the structural connectome using DTI tractography analysis were recently found in adult patients following left temporal lobe epilepsy surgery with significantly decreased global connectivity in whole brain and increased local connections in the default mode network [DeSalvo et al., 2014]. This study suggested that such increases in local connectivity may be due to a compensatory (or maladaptive) mechanism by which overall neural connectivity is maintained despite the loss of connections through important brain regions. Similarly, it was found that patients with unilateral medial TLE associated with hippocampal sclerosis had a paradoxical increase of local connectivity in ipsilateral limbic system including insular, superior temporal region and thalamus, suggesting that medial TLE may not only affect the hippocampus but also may reorganize axonal connections in the
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network of limbic structures [Bonilha et al., 2012]. Compared with these previous studies, our study revealed a unique pattern of increased post-surgical connectivity in the hemisphere contralateral to the surgical resection. This pattern was most prominent in specific frontal regions and not as widespread as in previous studies, mentioned above, which included mostly left TLE populations. Also, our results could not reproduce a previous finding where adults with recurrent seizures after TLE surgery exhibited a higher structural connectivity between the contralateral temporal pole and parietal lobe [Bonilha et al., 2013]. It should be emphasized, however, that ours is a study on a younger population and, it is well known that age at surgery is a major influencing factor of brain plasticity.
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Nonetheless, the findings of our study add to the growing body of evidence indicating remote neural alterations associated with epilepsy surgery. A recent study of children with TLE [Skirrow et al., 2015], evaluating neuroanatomical predictors of long-term memory outcomes after temporal lobe resection, has demonstrated that post-operative improvements in memory functions typically were subserved by the non-resected hemisphere: visual memory gains after left-side resections, and verbal memory gains after right-side resections. Post-surgical cognitive improvement has been interpreted as the release of reserve capacities that were suppressed or irritated by epilepsy. Our preliminary analysis of functional correlates found a significant positive association between post-operative visual memory deficit and decrease of insular connectivity in the contralateral hemisphere. This finding may support the notion that post-operatively increased axons in contralateral insular connections may mediate such release of reserve to compensate for memory impairment associated with neuronal loss in the surgical hemisphere.
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Importantly, despite the variable seizure foci in our cohort, the findings appear to be relatively specific to a few brain regions: insular and frontal orbital cortex, and appear to involve those brain regions and pathways which are known to be involved in higher cognitive functions and emotional/behavioral processing/regulation/inhibition [Kurth et al., 2010; Treit et al., 2014]. Indeed, a recent tractography study demonstrated multiple fiber connections originating from the insular cortex to limbic, paralimbic, inferior frontal, parietal and posterior temporal cortices in-vivo [Cerliani et al., 2012]. Thus, increased insular connectivity may be associated with and/or reflect alterations in multiple cognitive domains including working memory, language, speech and attention [Seeley et al., 2007]. The salience network consisting of limbic and paralimbic regions (including insula, frontal orbital, thalamus, caudate, amygdala and temporal pole showing post-operative connectivity increase in Figure 1) is thought to be important for the integration of internal states and external stimuli in the service of guiding behavior, and to act as a switch for mediating the activity of other neural networks such as the default mode network and the executive control network [Seeley et al., 2007; Uddin et al., 2013]. This network is also believed to be important for the development of emotion and behavioral control systems and therefore could affect neurocognitive and behavioral outcomes. Therefore, the functional correlates of this network reorganization as a result of increased connectivity in insular cortex need to be integrated in future studies in order to fully evaluate the post-operative increase as an effective imaging marker to predict specific types of post-operative neuropsychological outcomes in children undergoing epilepsy surgery.
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The present study was limited by a small sample size to investigate functional correlates of other interregional connections showing significant post-operative connectivity changes reported in Figure 1. It should be noted that our pre- and post-operative neuro-psychological data are relatively small, therefore, no definitive association between the increased connectivity and the improved memory may be drawn from this study. The fact that all but one of our 7 patients who underwent pre- and post-operative neuro-psychological assessement were seizure-free after surgery suggests that the observed connectivity increases are not due to continuing epileptogenicity but more likely reflect structural plasticity. Our limited data also showed that only two (index: 13,35) of the 7 patients showed apparently increased connectivity scores correlated with slightly improved cognitive functions. Therefore, whether post-surgical connectivity increases are indeed causally related to the improvement in some cognitive functions after surgery, remains to be determined in a larger patient group, prospectively. Also, the present study found no correlation between the time of post-operative MRI and the change in post-operative connectivity in the whole group (Figure 4). Most of the post-operative MRIs were performed 5–10 months after surgery; longer follow-up time was obtained in a few cases, but these showed no or minimal increases as opposed to several cases obtained with a shorter follow-up. Thus, the present study does not provide compelling data that longer post-operative interval before MRI would lead to larger increases in connectivity; however, this question can only be answered definitively by obtaining long-term, post-operative follow-up MRI data in the future.
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More importantly, low resolution of discrete nodes (i.e., small number of ROIs and poor spatial resolution of DTI) could be suboptimal to investigate the inter-regional connections showing significant correlations. This is a challenging issue regarding graph theoretical analysis of connectome data, as the large number of ROIs increases the number of comparisons, which may lead to an overly prohibitive corrected threshold with a very high likelihood of type II errors. Although the present study could not address the causal relationship between post-operative connectivity changes and seizure outcome, our data suggest that post-operative seizures may interfere with the plasticity of axonal connections in the contralateral hemisphere. Further research needs to evaluate causal associations between our findings and resective epilepsy surgery at higher spatial resolution [Zalesky et al., 2010; Ge et al., 2015].
Conclusion
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Post-operative increases in structural connectivity suggest short-term plasticity in the contralateral hemisphere of the children with medically refractory epilepsy, particularly in children with frontal resection. Such plasticity was most pronounced in specific frontal and insular connections, which were strongly associated with larger resection volumes and younger ages. Differential association between connectivity and cognitive function may be suggestive of distinct neural substrates for post-operative compensation mechanisms in the non-surgical hemisphere.
Supplementary Material Refer to Web version on PubMed Central for supplementary material.
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Acknowledgments Special thanks to Mrs. Alanna Marie Carlson and Dr. Robert Rothermel for clinical neuropsychological assessment. This study was funded by a grant from National Institute of Neurological Disorders and Stroke (R01-NS089659 to J.J). All authors would like to thank all participants and their families for their time and interest in this study.
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Hermann BP, Seidenberg M, Bell B. The neurodevelopmental impact of childhood onset temporal lobe epilepsy on brain structure and function and the risk of progressive cognitive effects. Prog Brain Res. 2002; 135:429–38. [PubMed: 12143361] Jeong JW, Asano E, Yeh FC, Chugani DC, Chugani HT. Independent component analysis tractography combined with ball and stick model to isolate intra-voxel crossing fibers of the corticospinal tracts in clinical diffusion MRI. Mag Reson Med. 2013; 70:441–53. Kurth F, Zilles K, Fox PT, Laird AR, Eickhoff SB. A link between the systems: functional differentiation and integration within the human insular revealed by meta-analysis. Brain Struct Funct. 2010; 214:519–34. [PubMed: 20512376] Lespinet V, Bresson C, N’Kaua B, Rougier A, Claverie B. Effect of age of onset of temporal lobe epilepsy on the severity and the nature of preoperative memory deficits. Neuropsychologia. 2002; 40:1591–600. [PubMed: 11985841] Maccotta L, Buckner RL, Gilliam FG, Ojemann JG. Changing frontal contributions to memory before and after medial temporal lobectomy. Cereb Cortex. 2007; 17:443–56. [PubMed: 16547345] Pataraia E, Billingsley-Marshall RL, Castillo EM, Breier JI, Simos PG, Sarkari S, Fitzgerald M, Clear T, Papanicolaou AC. Organization of receptive language-specific cortex before and after left temporal lobectomy. Neurology. 2005; 64:481–7. [PubMed: 15699379] Seeley WW, Menon V, Schatzberg AF, Keller J, Glover GH, Kenna H, Reiss AL, Greicius MD. Dissociable intrinsic connectivity networks for salience processing and executive control. J Neurosci. 2007; 27:2349–56. [PubMed: 17329432] Skirrow C, Cross H, Harrison S, Cormack F, Harkness W, Coleman R, Meierotto E, Gaittino J, VarghaKhadem F, Baldeweg T. Temporal lobe surgery in childhood and neuroanatomical predictors of long-term declarative memory outcome. Brain. 2015; 138:80–93. [PubMed: 25392199] Treit S, Chen Z, Rasmussen C, Beaulieu C. White matter correlates of cognitive inhibition during development: a diffusion tensor imaging study. Neurioscience. 2014; 276:87–97. Tzourio-Mazoyer N, Landeau B, Papathanassiou D, Crivello F, Etard O, Delcroix N, Mazoyer B, Jolit M. Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage. 2002; 15:273–89. [PubMed: 11771995] Uddin LQ, Supekar K, Lynch CJ, Khouzam A, Phillips J, Feinstein C, Ryali S, Menon V. Salience network-based classification and prediction of symptom severity in children with autism. JAMA Psychiatry. 2013; 70:869–79. [PubMed: 23803651] Wong SW, Jong L, Bandur D, Bihari F, Yen YF, Takahashi AM, Lee DH, Steven DA, Parrent AG, Pigott SE, Mirsattari SM. Cortical reorganization following anterior temporal lobectomy in patients with temporal lobe epilepsy. Neurology. 2009; 73:518–25. [PubMed: 19687453] Zalesky A, Fornito A, Harding IH, Cocchi L, Yucel M, Pantelis C, Bullmore ET. Whole-brain networks: does the choice of nodes matter? Neuroimage. 2010; 50:970–83. [PubMed: 20035887]
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Figure 1.
Interregional connections showing significant interaction of group (fontal vs. extra-frontal resection) by time (pre- vs. post-operative) in the connectivity score of individual ROI obtained from a two-way mixed ANOVA at p-value < 0.05. Each green sphere indicates the location of each ROI (node). The thickness of each tube scales the F-statistic value of the post-operative connectivity score increase measured at each interregional connection (i.e., the thicker the higher F-statistic indicating more increase). No node showed significant decrease in post-operative connectivity score at p-value < 0.05.
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Figure 2.
Two pairwise axonal connections showing statistically significant associations between postoperative connectivity score change (post-operative value – pre-operative value) and resection volume at p-value < 0.05. A) Insular–inferior frontal operculum. B) Superior frontal orbital-mid frontal orbital. Post-operative connectivity increases were most robust in younger children (age < 10 years) with frontal resection (marked by circles). At each plot, the fitted line is displayed with its 95% confidence range.
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Figure 3.
Box plot showing minimum, first quartile, median, third quartile and maximum value of interregional connectivity score obtained from three age-subgroups (age < 5 years old, age 5–10 years old, age > 10 years old) of pre-operative, post-operative and healthy control groups. A) insular–inferior frontal operculum. B) superior frontal orbital-mid frontal orbital. “* and o” indicate the outliers of each subgroup. Compared with healthy controls, significant increases in interregional streamlines of post-operative subgroups were found in both interregional connections (p-value corrected for multiple comparisons = 0.03/0.009 for
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insular–inferior frontal operculum/superior frontal orbital-mid frontal orbital, respectively). In subgroup analysis of age 5–10 year old, post-operative connectivity score was significantly increased compared with pre-operative and healthy control (i.e., one-way ANOVA p-value corrected by age = 0.03/0.05 and 0.04/0.05 for insular–inferior frontal operculum/superior frontal orbital-mid frontal orbital, respectively).
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Figure 4.
Correlation of post-operative change in connectivity score and post-operative DTI time. A) insular–inferior frontal operculum (Spearman’s ρ=−0.07, p-value=0.54). B) superior frontal orbital-mid frontal orbital (Spearman’s ρ=0.02, p-value=0.88).
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Figure 5.
Correlation of post-operative change in connectivity score and neuropsychological score (i.e. post-operative value – pre-operative value). A) Connectivity score of insular–inferior frontal operculum and cognitive score of visual memory (Spearman’s ρ=0.85, p-value=0.03). B) Connectivity score of superior frontal orbital-mid frontal orbital and cognitive t-score of GDS delay (Spearman’s ρ=0.89, p-value=0.01).
Author Manuscript Author Manuscript Hum Brain Mapp. Author manuscript; available in PMC 2017 November 01.
Author Manuscript
3.8
13.4
2
3
12.1
9.7
10.9
16.1
8.1
3.0
4.7
3.1
13.9
11.7
20.0
16.7
1.8
3.1
13.6
6.6
2.6
15.2
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
M
7.0
11.6
9
8
10
M
7.3
12.8
7
Hum Brain Mapp. Author manuscript; available in PMC 2017 November 01.
M
F
M
M
M
M
M
F
F
F
M
M
F
M
M
F
F
F
M
F
F
F
3.0
11.3
6
F
F
M
M
gender
5
9.2
1.3
1
4
age
CP with GTC
ES,CP
CP
CP (past GTC)
CP
ES
CP
GTC
CP
CP
ES
CP
CP
GTC
CP
CP, GTC
ES
CP
CP
CP (past GTC)
CP
SP
CP
ES
CP
SP
ES
SP
Type
RTPO
RFrTP
LFrT
LT
RFrP
RTO
RFrP
LT
RFrP
RT
LTPO
RT
LT
RFr
LFr
RFrTO
RFr
LT
LT
RT
LP
LFr
LT
LFrTO
LT
LFr
LT
LT
Resection location
12.2
2.3
5.9
6.6
1.1
1.4
13.7
6.0
5.7
9.9
1.7
2.7
1.6
7.6
5.1
4.9
7.9
3.1
11.5
1.0
5.8
1.3
8.3
2.5
0.8
1.4
3.4
1.2
Epilepsy duration
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index
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Clinical data of the patient with epilepsy surgery.
encephalomalacia
fcd
fcd
fcd
gliosis
fcd
ulegyria
ulegyria
fcd(T)/gliosis(Fr)
gliosis
gliosis
meningiomatosis NF2
gliosis
fcd
gliosis
fcd
gliosis
Hippocampal sclerosis
tumor
tumor
fcd
tumor
tumor
gliosis
tumor
inflammation
gliosis
fcd
Histology
not free
free
not free
not free
free
free
not free
free
free
free
free
free
not free
free
free
free
free
free
free
free
free
free
free
not free
free
not free
not free
not free
Seizure Outcome
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Table 1 Jeong et al. Page 18
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5.6
5.6
7.4
31
32
33
34 M
M
F
F
M
F
M
ES
GTC
CP
CP
GTC
CP
ES,CP
RFr
LFrTP
LT
LFrT
RFr
LT
RT
Resection location
6.0
4.4
4.2
0.6
7.6
8.3
2.8
Epilepsy duration
fcd
arachnoid cyst+hippocampal sclerosis
hippocampal sclerosis
inflammation
microgyria/fcd
fcd
fcd
Histology
free
free
free
not free
not free
free
free
Seizure Outcome
ES=epileptic spasms, SP=simple partial seizure, CP=complex partial seizure, GTC=generalized tonic-clonic seizure, Fr=Frontal, T=temporal, P=parietal, O=occipital, fcd=focal cortical dysplasia. Age and epilepsy duration are reported in years.
6.6
8.1
30
35
3.4
14.3
29
Type
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gender
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age
Author Manuscript
index
Jeong et al. Page 19
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Table 2
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Results of permuted Spearman’s ρ-rank analyses between post-operative connectivity score changes and different types of regressors.
Regressor
regressor
insular–inferior frontal operculum
superior frontal orbitalmid frontal orbital
ρ-coefficient
0.3997
0.4021
p-value
0.0180*
0.0166*
ρ-coefficient
−0.3413
−0.3388
p-value
0.0448*
0.0465*
ρ-coefficient
0.2853
0.3441
p-value
0.2832
0.1919
ρ-coefficient
0.1807
0.2273
p-value
0.4575
0.3494
ρ-coefficient
−0.1455
0.2909
p-value
0.6734
0.3864
ρ-coefficient
0.5913
0.4475
p-value
0.0028*
0.0283*
correlation measure
resection volumes of all patients
ages of all patients
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post-operative connectivity score changes (i.e, postoperative scores – preoperative scores)
frontal resection volumes
extra-frontal resection volumes resection volumes of not seizurefree patients resection volumes of seizure-free patients
*
indicates p-value
Hum Brain Mapp. Author manuscript; available in PMC 2017 November 01. Published in final edited form as: Hum Brain Mapp. 2016 November ; 37(11): 3946–3956. doi:10.1002/hbm.23287.
Post-operative axonal changes in the contralateral hemisphere in children with medically refractory epilepsy: a longitudinal DTI connectome analysis Jeong-Won Jeong1,2, Eishi Asano1,2, Csaba Juhász1,2, Michael E. Behen1,2, and Harry T. Chugani3,4
Author Manuscript
1Departments
of Pediatrics and Neurology, School of Medicine, Wayne State University, Detroit,
MI, USA 2Translational 3Department 4Thomas
Imaging Laboratory, PET Center, Children’s Hospital of Michigan, Detroit, MI, USA
of Neurology, Nemours DuPont Hospital for Children, Wilmington, DE, USA
Jefferson University School of Medicine, Philadelphia, PA, USA
Abstract
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To determine brain plasticity changes due to resective epilepsy surgery in children, we performed a longitudinal connectome analysis on the pattern of axonal connectivity in the contralateral hemisphere. Pre- and post-operative diffusion tensor imaging (DTI) data were acquired from 35 children with intractable focal epilepsy. A total of 54 brain regions of interest (ROIs) were generated in the hemisphere contralateral to the resection. Within a 54×54 connectivity matrix, a pair-wise connectivity score was calculated for each connection between two ROIs, based on the DTI fiber streamline number in each connection. A permuted Spearman’s ρ-rank analysis was used to identify specific inter-regional connections showing a significant association between the post-operative change of connectivity score and clinical variables. Nineteen connections in the contralateral hemisphere showed postoperative increases in the strength of connectivity. Postoperative increase in connectivity between insular-inferior frontal operculum regions as well as that between superior frontal orbital-mid frontal orbital regions were both significantly associated with a larger surgical resection volume (ρ>+0.40) and a younger patient age (ρ>−0.34). These increases were more robust in patients with frontal resection and in those achieving seizure freedom. Neuropsychological evaluation on subsets of patients revealed that such increases in connectivity were associated with preserved or improved cognitive functions such as visual memory and planning. Resective epilepsy surgery may lead to increased contralateral axonal connectivity in children with focal epilepsy. Our data lead to a hypothesis that such increased connectivity may be an imaging marker of post-operative brain plasticity to compensate for cognitive function.
Corresponding author: Jeong-Won Jeong, PhD, Departments of Pediatrics and Neurology, Wayne State University School of Medicine, PET Center, Children’s Hospital of Michigan, 3901 Beaubien St., Detroit, MI, 48201, Phone: 313-993-0258; Fax: 313-966-9228; [email protected]. The authors declare no conflicts of interest.
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Keywords Diffusion tensor imaging; Epilepsy surgery; Axonal plasticity; Contralateral reorganization
Introduction
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Medically refractory epilepsy may lead to progressive deterioration of the function of affected brain structures concurrent with compensatory changes (i.e., functional reorganization) in non-epileptic areas [Lespinet et al., 2002; Hermann et al., 2002]. However, persistent focal epilepsy early in development can interfere with the brain’s potential for functional reorganization [Chugani et al., 1996]. In addition, ictal and interictal epileptiform activities can modify neural networks, resulting in neuro-cognitive and behavioral disturbances [Benuzzi et al., 2004]. Indeed, emerging data suggest that repeated seizures may reconfigure long-range connections between neuronal populations in different parts of the brain [Bonilha et al., 2012; Bonilha et al., 2013; DeSalvo et al., 2014]. The fact that some children with intractable epilepsy undergo large cortical resections provides a unique opportunity to evaluate post-operative reorganizational changes of white matter as well as the clinical correlates of such reorganization in these children.
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Previous studies using functional MRI (fMRI) have reported functional changes before and after epilepsy surgery, mainly focusing on cognition including language and memory functions [Billingsley et al., 2001; Pataraia et al., 2005; Backes et al., 2005; Maccotta et al., 2007; Wong et al., 2009]. Recently, it was also demonstrated that anterior temporal lobectomy elicited alterations allowing behavioral and hemodynamic improvement in emotion recognition [Benuzzi et al., 2014], suggesting that successful temporal lobectomy may activate the process of functional plasticity in early-onset medial temporal lobe epilepsy (TLE). These studies consistently supported the notion that post-operative functional reorganization may involve both intra- and inter-hemispheric reorganization of specific areas.
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A recent study using diffusion tensor imaging (DTI) showed that the axonal integrity of temporal lobe-related fiber tracts was decreased for a period of >1 year after surgery in the ipsilateral hemisphere of TLE adult patients, indicating Wallerian degeneration of the affected fiber bundles following surgical disconnection of neurons [Faber et al., 2013]. However, whether surgical intervention affects the trajectory of axonal reorganization in the contralateral hemisphere of children with TLE or extra-TLE remains unknown. This is of particular relevance since the integrity of the contralateral hemisphere is important in impacting post-surgical neuro-cognitive outcome [Althausen et al., 2013]. The aim of the present study was to investigate the potential effect of resective epilepsy surgery on the pattern of axonal connectivity in the contralateral hemisphere in children with intractable focal epilepsy. Toward this goal, we applied a state-of-the-art whole brain connectome analysis method. Connectome analysis is a recently developed method using structural and functional neuroimaging techniques including DTI and fMRI, focused on quantifying and analyzing properties of complex large-scale connectivity of brain networks. The present study views epilepsy as a network disease and models the epileptic brain as a Hum Brain Mapp. Author manuscript; available in PMC 2017 November 01.
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network or a graph using a connectome represented by a collection of nodes (i.e., cortical and subcortical regions) and links (i.e., axonal fiber counts between nodes) in order to examine the post-operative changes in nodal links in the contralateral (non-operated) hemisphere. The analyses performed in this study were driven by the hypothesis that surgical intervention may differentially drive axonal reorganization of the contralateral hemisphere depending on age as well as volume and location of surgical resection. We hypothesized that: 1) larger surgical resections will be associated with more altered, possibly increased, nodal links in specific regions of the contralateral hemisphere indicating a distinctive pattern of axonal plasticity after surgery, and 2) such changes will be associated with post-operative changes in specific cognitive domains. We were particularly interested in the effect of resection of the frontal lobe, the largest among the four lobes in the human brain.
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Methods Subjects Thirty-five children with epilepsy who underwent epilepsy surgery between 2008 and 2014 at the Children’s Hospital of Michigan (Detroit) were retrospectively selected for the study (mean age ± standard deviation [SD]: 8.7±4.9 years, 19 boys). Resections included the frontal lobe in 16 children and were extra-frontal (temporal, parietal, or/and occipital) in 19 (Table 1). The study excluded surgical patients who had severe, extensive bilateral lesions such as tuberous sclerosis complex, which might affect, regardless of resective surgery, the tracking of white matter terminals in connectome analysis in the contralateral hemisphere.
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The hemisphere contralateral to the resection was normal on MRI in all children. Twenty children had surgery in the left hemisphere and 15 children in the right hemisphere. The surgical outcome was assessed at least 1 year after surgery (time of post-surgical follow-up= 3.2±1.5 years), categorized into seizure-free (n=24) and not seizure-free groups (n=11). The two groups did not differ for duration of epilepsy (4.7±3.0 and 4.7±4.5 years; p-value=0.99), age at onset of epilepsy (4.2±3.9 and 2.9±3.6 years; p-value=0.32), or resection volume (50.6±50.1 and 57.8±50.9 ×1000mm3, p-value=0.62).
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Thirty-six children with no history of medical, psychiatric disease and normal cognitive function, defined by measured global cognition, language, and adaptive behavior (communication, daily living, socialization, motor) skills within normal limits (standard score > 85), were recruited as healthy controls (age: 9.1±5.2 years, 21 boys). The two groups (control, epilepsy) did not differ by age (p-value= 0.52) or gender distribution (p-value= 0.77). The study was approved by Wayne State University’s Institutional Review Board and written informed consent was obtained from all parents/guardians. Neuropsychological assessment All children underwent comprehensive neurocognitive evaluations, including assessment of verbal and nonverbal intellectual functioning (using the age appropriate Wechsler measures, WPPSI-3 for children 6 years of age or younger; WISC-IV for children 6 years of age and older); receptive and expressive language processing (using the appropriate Comprehensive
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Evaluation of Language Fundamentals, CELF-Preschool for children 5 years, expressive and receptive language indices); executive functions (in children 5 years of age and up, including sustained attention and behavioral control; Gordon Diagnostic Scale Delay and Vigilance Tasks), verbal and visual memory (in children 5 years and up, Wide Range Assessment of Memory and Learning-2nd Edition), and manual dexterity for both the dominant and non-dominant hands (Grooved Pegs Test for children 5 years and up, or Purdue Pegboard for children younger than 5 years). All of the above measures have established psychometric properties and are widely used in clinical and research samples. All assessments were completed within two weeks of the MRI scans. MRI acquisition
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All MRI scans were performed at the Children’s Hospital of Michigan on a 3T GE-Signa scanner (GE Healthcare, Milwaukee, WI) equipped with an 8-channel head coil and ASSET. DTI was acquired with a multi-slice single shot diffusion weighted echo-planar-imaging sequence at TR = 12,500ms, TE = 88.7ms, FOV = 24cm, 128×128 acquisition matrix (nominal resolution = 1.89mm), contiguous 3mm thickness in order to cover entire axial slices of whole brain using 55 isotropic gradient directions with b= 1000s/mm2, one b=0 acquisition, and number of excitations = 1. For anatomical reference, a three-dimensional fast spoiled gradient echo sequence (FSPGR) was acquired for each participant at TR/TE/TI of 9.12/3.66/400 ms, slice thickness of 1.2 mm, and planar resolution of 0.94×0.94 mm2. Because the scans for children with epilepsy surgery were clinical MRI studies, sedation was used as necessary. Patients had pre-operative MRI 3.1 months (SD: 2.9) prior to surgery and post-operative MRI 8.4 months after surgery (SD: 6.2).
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None of the control children were sedated for the MRI. They were scanned while sleeping, and monitored for movement during scan. For each of 36 controls, a, b = 0 image was selected as a target image for co-registering the 55 b = 1000 images. Six motion parameters including three translation and three rotation parameters in x,y,z were estimated for each b = 1000 image using SPM 8 co-registration toolbox (www.fil.ion.ucl.ac.uk/spm). For each parameter, the absolute displacement between adjacent images was averaged to assess the degree of head motion [Ling et al., 2012]. The average value of three average translations was finally threshold at no greater than 3 mm to be considered as no significant head motion. Thirty-three of 36 controls were considered to have no significant movements and utilized for the statistical comparison with epilepsy groups. MRI analysis
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Using a drawing tool in MRIcro software (www.mricro.com), one investigator (J.J) manually demarcated the volume of surgically removed tissue on the post-surgical FSPGR image in order to estimate the resection volume from individual patients. For DTI of each subject, an independent component analysis with ball and stick model (ICA+BSM) tractography [Jeong et al., 2013] was applied for whole brain tractography. This technique was designed to minimize the intra-voxel crossing fiber problem and isolate the orientations of up to three crossing fiber bundles at every voxel. Before performing ICA+BSM tractography analysis for the structural connectivity, the NIH TORTOISE DIFF_PREP software package (https://science.nichd.nih.gov/confluence/display/nihpd/TORTOISE) was
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used to correct motion and eddy current distortion in the DTI data. Whole brain streamline tractography was then performed using ICA+BSM to reconstruct up to three crossing streamlines at every voxel of fractional anisotropy > 0.20. At every voxel, the first eigenvector of the resulting stick components having a fractional ratio > 0.05 was considered as the reconstructed fiber orientations and then utilized for the streamline tractography at step size = 0.2 voxel width, turning angle threshold = 60°, and maximal length = 250 mm.
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Using SPM DARTEL procedure [Ashburner, 2007], three age-specific T1 templates (≤ 5years, 6–10 years, ≥ 11 years) were created from T1 images of healthy controls. To parcellate brain regions of interest in each age-specific T1 template, the present study utilized an SPM automated anatomical labeling atlas (AAL, http://www.gin.cnrs.fr/spip.php) consisting of 116 regional parcellations in MNI template space. This MNI template was spatially normalized to each of three T1 templates using linear normalization, diffeomorphic normalization and iterative averaging [Akiyama et al., 2013]. The resulting age-specific AAL template was deformed into a T1 image of individual age-matched patient using SPM 12 DARTEL deformation toolbox (www.fil.ion.ucl.ac.uk). The deformed AAL template in T1 image was finally co-registered using SPM 12 to a b0 image in order to generate a total of 54 regions of interest (ROIs) in the contralateral non-operated hemisphere. Every pair of the resulting cortical regions was applied to whole brain tractography in order to sort specific streamlines connecting a pair of two regions, resulting in a 54×54 connectivity matrix in which the elements quantified the pair-wise connectivity scores, based on the fiber streamline number in each connection normalized by total volume of the applied two ROIs. The anatomical descriptions and the corresponding labels of 54 ROIs are presented in supplementary table 1 and also available in previous studies [Tzourio-Mazoyer et al., 2002, Diebrichesn J, 2006].
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Statistical analysis
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A two-way mixed ANOVA with between-subject ‘random’ factor (frontal vs. extra-frontal resection) and within-subject ‘fixed’ factor (pre- vs. post-operative) was performed for the connectivity score of each element in order to identify specific elements showing significant group-by-time interaction. Then, a permuted Spearman’s ρ-rank analysis was used to identify specific elements showing a significant association between the post-operative change of connectivity score and the resected tissue volume. For each connectivity element, we permuted the order of individual patient data: (1) post-operative score change (i.e.: postoperative score – pre-operative score) and (2) resection volume, picked 50% of total data randomly in the framework of bootstrapping analysis, and calculated Spearman’s ρ-rank correlation coefficients of post-operative score change with two regressors (resection volume and age) to identify the nodes of elements showing significant resection-age-related connectivity changes. We repeated 1000 trials for each of different subgroups (i.e.: whole group, frontal resection, extra-frontal resection, seizure-free and not seizure-free). The ρcoefficients and p-values of whole group were averaged to find out the nodes of elements showing significant correlation at p-value < 0.05. Subsequently, the post-operative connectivity score changes of the resulting nodes were correlated with post-operative neuropsychological assessment scores to investigate their functional relevance to neurocognitive outcomes.
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Results Surgical resections were tailored to include both visible individual pathology (if present) and epileptogenic regions as defined by intracranial EEG; thus the resections were highly variable across patients [Asano et al., 2009]. The highest overlap of tissue removal across all individuals was located in frontal and temporal lobes in either hemisphere. Resection volume did not differ by the side of surgery (mean volume: 49.9/70.8 ×1000mm3 for left/ right surgery; p-value = 0.10). Age at surgery did not correlate with total resection volume (Spearman’s ρ = −0.21, p-value = 0.22).
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In the comparison between pre- and post-operative connectivity score, one-way ANOVA corrected by age found that post-operative score significantly increased in 29 interregional connections widely distributed in whole brain (supplementary Figure 1). In contrast, nineteen inter-regional connections showed a significant interaction of group (frontal resection vs. extra-frontal resection) with time (pre-operative vs. post-operative) obtained from the two-way mixed ANOVA at p-value < 0.05 (Figure 1). The strength of connectivity in fifteen connections was larger in patients who underwent frontal lobe resection, whereas the strength in four connections was larger in those who underwent extra-frontal lobe resection. Compared with extra-frontal resection, frontal resection showed more widely spread nodes with significantly increased post-operative connectivity score.
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Among those 19 connections, two interregional connections, insular-inferior frontal operculum and superior frontal orbital-mid frontal orbital showed a statistically significant association between post-operative connectivity score change and corresponding volume of tissue resection (Figure 2). The post-operative connectivity score changes of these connections were positively correlated with the resection volumes (i.e., larger resection associated with more increased connectivity, ρ=0.40/0.41, p-value =0.02/0.02 for insular– inferior frontal operculum/superior frontal orbital-mid frontal orbital, Table 2). These changes were also negatively correlated with age (i.e., younger age associated with more increased connectivity, ρ=−0.34/−0.34, p-value =0.05/0.05 for insular-inferior frontal operculum/superior frontal orbital-mid frontal orbital), and were especially robust in younger children (age < 10 years) (see Figure 2 and Table 2). Also, more robust correlations were observed in children with frontal resection compared with extra-frontal resection (Table 2). In addition, the significant positive correlations between post-operative connectivity score changes and the resection volumes were present in the seizure-free group (i.e., ρ=0.59/0.48, p-value =0.00/0.03 for insular-inferior frontal operculum/superior frontal orbital-mid frontal orbital) but was not significant in patients who had post-operative seizures (Table 2). No significant negative correlations were found between the postoperative connectivity changes and the resection volumes. The connectivity scores of these two connections were measured within normal limits before surgery and increased after surgery, as indicated by the significantly higher interregional streamlines compared with healthy controls (Figure 3). Compared with healthy controls, significant increases in interregional streamlines of post-operative subgroups were found in both interregional connections (i.e., univariate post-hoc p-value corrected using Bonferroni for multiple comparisons = 0.03/0.009 for insular–inferior frontal operculum/superior frontal
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orbital-mid frontal orbital, respectively). In subgroup analysis of age 5–10 year old using one-way ANOVA with covariate of age, post-operative connectivity score was significantly increased compared with pre-operative and healthy control (i.e., p-value= 0.03/0.05 and 0.04/0.05 for insular–inferior frontal operculum/superior frontal orbital-mid frontal orbital, respectively). There was no correlation between the time of post-operative DTI time and the change in post-operative connectivity score in the whole seizure group (Figure 4, ρ-coefficient/pvalue=−0.07/0.54 and 0.02/0.88 for insular-Frontal Inf Oper and Frontal Sup Orb-Frontal Mid Orb, respectively). The most increases were observed between 5 and 12 months of postoperative DTI.
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In a subgroup of patients where both pre- and post-operative neuro-cognitive scores were available (n=7, index: 4,9,11,13,19,22, 35 in Table 1), post-operative changes in two cognitive domains, visual memory and Gordon Diagnostic Scale Delay score (planning/ organization skills), were positively correlated with post-operative connectivity increases in insular-inferior frontal operculum and superior frontal orbital-mid frontal orbital at pvalue=0.03 and 0.01, respectively (i.e., increased connectivity related to better postoperative function, Figure 5). Note that two seizure-free children (index: 13,35) showed apparently increased connectivity scores associated with improved cognitive functions.
Discussion
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The present study demonstrates the potential use of whole brain connectome analysis as a new tool to assess short-term post-operative plasticity in the contralateral hemisphere of children with drug-resistant focal epilepsy. In group comparisons, we found increased postoperative connectivity in a number of connections in the contralateral hemisphere, which differed according to the location of resections (frontal vs. extra-frontal, see Figure 1). Among these, two connections in insular and frontal orbital cortex demonstrated postoperative increases associated with a larger volume of surgical resection and younger age as well as seizure-free outcome. Although preliminary, the results also reveal an association between increased connectivity and preserved or improved cognitive functions in specific domains. Thus, postoperative changes in DTI-detected connectivity may be an effective imaging marker of functionally relevant structural brain plasticity associated with surgical intervention.
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Alterations in the structural connectome using DTI tractography analysis were recently found in adult patients following left temporal lobe epilepsy surgery with significantly decreased global connectivity in whole brain and increased local connections in the default mode network [DeSalvo et al., 2014]. This study suggested that such increases in local connectivity may be due to a compensatory (or maladaptive) mechanism by which overall neural connectivity is maintained despite the loss of connections through important brain regions. Similarly, it was found that patients with unilateral medial TLE associated with hippocampal sclerosis had a paradoxical increase of local connectivity in ipsilateral limbic system including insular, superior temporal region and thalamus, suggesting that medial TLE may not only affect the hippocampus but also may reorganize axonal connections in the
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network of limbic structures [Bonilha et al., 2012]. Compared with these previous studies, our study revealed a unique pattern of increased post-surgical connectivity in the hemisphere contralateral to the surgical resection. This pattern was most prominent in specific frontal regions and not as widespread as in previous studies, mentioned above, which included mostly left TLE populations. Also, our results could not reproduce a previous finding where adults with recurrent seizures after TLE surgery exhibited a higher structural connectivity between the contralateral temporal pole and parietal lobe [Bonilha et al., 2013]. It should be emphasized, however, that ours is a study on a younger population and, it is well known that age at surgery is a major influencing factor of brain plasticity.
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Nonetheless, the findings of our study add to the growing body of evidence indicating remote neural alterations associated with epilepsy surgery. A recent study of children with TLE [Skirrow et al., 2015], evaluating neuroanatomical predictors of long-term memory outcomes after temporal lobe resection, has demonstrated that post-operative improvements in memory functions typically were subserved by the non-resected hemisphere: visual memory gains after left-side resections, and verbal memory gains after right-side resections. Post-surgical cognitive improvement has been interpreted as the release of reserve capacities that were suppressed or irritated by epilepsy. Our preliminary analysis of functional correlates found a significant positive association between post-operative visual memory deficit and decrease of insular connectivity in the contralateral hemisphere. This finding may support the notion that post-operatively increased axons in contralateral insular connections may mediate such release of reserve to compensate for memory impairment associated with neuronal loss in the surgical hemisphere.
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Importantly, despite the variable seizure foci in our cohort, the findings appear to be relatively specific to a few brain regions: insular and frontal orbital cortex, and appear to involve those brain regions and pathways which are known to be involved in higher cognitive functions and emotional/behavioral processing/regulation/inhibition [Kurth et al., 2010; Treit et al., 2014]. Indeed, a recent tractography study demonstrated multiple fiber connections originating from the insular cortex to limbic, paralimbic, inferior frontal, parietal and posterior temporal cortices in-vivo [Cerliani et al., 2012]. Thus, increased insular connectivity may be associated with and/or reflect alterations in multiple cognitive domains including working memory, language, speech and attention [Seeley et al., 2007]. The salience network consisting of limbic and paralimbic regions (including insula, frontal orbital, thalamus, caudate, amygdala and temporal pole showing post-operative connectivity increase in Figure 1) is thought to be important for the integration of internal states and external stimuli in the service of guiding behavior, and to act as a switch for mediating the activity of other neural networks such as the default mode network and the executive control network [Seeley et al., 2007; Uddin et al., 2013]. This network is also believed to be important for the development of emotion and behavioral control systems and therefore could affect neurocognitive and behavioral outcomes. Therefore, the functional correlates of this network reorganization as a result of increased connectivity in insular cortex need to be integrated in future studies in order to fully evaluate the post-operative increase as an effective imaging marker to predict specific types of post-operative neuropsychological outcomes in children undergoing epilepsy surgery.
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The present study was limited by a small sample size to investigate functional correlates of other interregional connections showing significant post-operative connectivity changes reported in Figure 1. It should be noted that our pre- and post-operative neuro-psychological data are relatively small, therefore, no definitive association between the increased connectivity and the improved memory may be drawn from this study. The fact that all but one of our 7 patients who underwent pre- and post-operative neuro-psychological assessement were seizure-free after surgery suggests that the observed connectivity increases are not due to continuing epileptogenicity but more likely reflect structural plasticity. Our limited data also showed that only two (index: 13,35) of the 7 patients showed apparently increased connectivity scores correlated with slightly improved cognitive functions. Therefore, whether post-surgical connectivity increases are indeed causally related to the improvement in some cognitive functions after surgery, remains to be determined in a larger patient group, prospectively. Also, the present study found no correlation between the time of post-operative MRI and the change in post-operative connectivity in the whole group (Figure 4). Most of the post-operative MRIs were performed 5–10 months after surgery; longer follow-up time was obtained in a few cases, but these showed no or minimal increases as opposed to several cases obtained with a shorter follow-up. Thus, the present study does not provide compelling data that longer post-operative interval before MRI would lead to larger increases in connectivity; however, this question can only be answered definitively by obtaining long-term, post-operative follow-up MRI data in the future.
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More importantly, low resolution of discrete nodes (i.e., small number of ROIs and poor spatial resolution of DTI) could be suboptimal to investigate the inter-regional connections showing significant correlations. This is a challenging issue regarding graph theoretical analysis of connectome data, as the large number of ROIs increases the number of comparisons, which may lead to an overly prohibitive corrected threshold with a very high likelihood of type II errors. Although the present study could not address the causal relationship between post-operative connectivity changes and seizure outcome, our data suggest that post-operative seizures may interfere with the plasticity of axonal connections in the contralateral hemisphere. Further research needs to evaluate causal associations between our findings and resective epilepsy surgery at higher spatial resolution [Zalesky et al., 2010; Ge et al., 2015].
Conclusion
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Post-operative increases in structural connectivity suggest short-term plasticity in the contralateral hemisphere of the children with medically refractory epilepsy, particularly in children with frontal resection. Such plasticity was most pronounced in specific frontal and insular connections, which were strongly associated with larger resection volumes and younger ages. Differential association between connectivity and cognitive function may be suggestive of distinct neural substrates for post-operative compensation mechanisms in the non-surgical hemisphere.
Supplementary Material Refer to Web version on PubMed Central for supplementary material.
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Acknowledgments Special thanks to Mrs. Alanna Marie Carlson and Dr. Robert Rothermel for clinical neuropsychological assessment. This study was funded by a grant from National Institute of Neurological Disorders and Stroke (R01-NS089659 to J.J). All authors would like to thank all participants and their families for their time and interest in this study.
References
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Figure 1.
Interregional connections showing significant interaction of group (fontal vs. extra-frontal resection) by time (pre- vs. post-operative) in the connectivity score of individual ROI obtained from a two-way mixed ANOVA at p-value < 0.05. Each green sphere indicates the location of each ROI (node). The thickness of each tube scales the F-statistic value of the post-operative connectivity score increase measured at each interregional connection (i.e., the thicker the higher F-statistic indicating more increase). No node showed significant decrease in post-operative connectivity score at p-value < 0.05.
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Figure 2.
Two pairwise axonal connections showing statistically significant associations between postoperative connectivity score change (post-operative value – pre-operative value) and resection volume at p-value < 0.05. A) Insular–inferior frontal operculum. B) Superior frontal orbital-mid frontal orbital. Post-operative connectivity increases were most robust in younger children (age < 10 years) with frontal resection (marked by circles). At each plot, the fitted line is displayed with its 95% confidence range.
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Figure 3.
Box plot showing minimum, first quartile, median, third quartile and maximum value of interregional connectivity score obtained from three age-subgroups (age < 5 years old, age 5–10 years old, age > 10 years old) of pre-operative, post-operative and healthy control groups. A) insular–inferior frontal operculum. B) superior frontal orbital-mid frontal orbital. “* and o” indicate the outliers of each subgroup. Compared with healthy controls, significant increases in interregional streamlines of post-operative subgroups were found in both interregional connections (p-value corrected for multiple comparisons = 0.03/0.009 for
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insular–inferior frontal operculum/superior frontal orbital-mid frontal orbital, respectively). In subgroup analysis of age 5–10 year old, post-operative connectivity score was significantly increased compared with pre-operative and healthy control (i.e., one-way ANOVA p-value corrected by age = 0.03/0.05 and 0.04/0.05 for insular–inferior frontal operculum/superior frontal orbital-mid frontal orbital, respectively).
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Figure 4.
Correlation of post-operative change in connectivity score and post-operative DTI time. A) insular–inferior frontal operculum (Spearman’s ρ=−0.07, p-value=0.54). B) superior frontal orbital-mid frontal orbital (Spearman’s ρ=0.02, p-value=0.88).
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Figure 5.
Correlation of post-operative change in connectivity score and neuropsychological score (i.e. post-operative value – pre-operative value). A) Connectivity score of insular–inferior frontal operculum and cognitive score of visual memory (Spearman’s ρ=0.85, p-value=0.03). B) Connectivity score of superior frontal orbital-mid frontal orbital and cognitive t-score of GDS delay (Spearman’s ρ=0.89, p-value=0.01).
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3.8
13.4
2
3
12.1
9.7
10.9
16.1
8.1
3.0
4.7
3.1
13.9
11.7
20.0
16.7
1.8
3.1
13.6
6.6
2.6
15.2
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
M
7.0
11.6
9
8
10
M
7.3
12.8
7
Hum Brain Mapp. Author manuscript; available in PMC 2017 November 01.
M
F
M
M
M
M
M
F
F
F
M
M
F
M
M
F
F
F
M
F
F
F
3.0
11.3
6
F
F
M
M
gender
5
9.2
1.3
1
4
age
CP with GTC
ES,CP
CP
CP (past GTC)
CP
ES
CP
GTC
CP
CP
ES
CP
CP
GTC
CP
CP, GTC
ES
CP
CP
CP (past GTC)
CP
SP
CP
ES
CP
SP
ES
SP
Type
RTPO
RFrTP
LFrT
LT
RFrP
RTO
RFrP
LT
RFrP
RT
LTPO
RT
LT
RFr
LFr
RFrTO
RFr
LT
LT
RT
LP
LFr
LT
LFrTO
LT
LFr
LT
LT
Resection location
12.2
2.3
5.9
6.6
1.1
1.4
13.7
6.0
5.7
9.9
1.7
2.7
1.6
7.6
5.1
4.9
7.9
3.1
11.5
1.0
5.8
1.3
8.3
2.5
0.8
1.4
3.4
1.2
Epilepsy duration
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index
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Clinical data of the patient with epilepsy surgery.
encephalomalacia
fcd
fcd
fcd
gliosis
fcd
ulegyria
ulegyria
fcd(T)/gliosis(Fr)
gliosis
gliosis
meningiomatosis NF2
gliosis
fcd
gliosis
fcd
gliosis
Hippocampal sclerosis
tumor
tumor
fcd
tumor
tumor
gliosis
tumor
inflammation
gliosis
fcd
Histology
not free
free
not free
not free
free
free
not free
free
free
free
free
free
not free
free
free
free
free
free
free
free
free
free
free
not free
free
not free
not free
not free
Seizure Outcome
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Table 1 Jeong et al. Page 18
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5.6
5.6
7.4
31
32
33
34 M
M
F
F
M
F
M
ES
GTC
CP
CP
GTC
CP
ES,CP
RFr
LFrTP
LT
LFrT
RFr
LT
RT
Resection location
6.0
4.4
4.2
0.6
7.6
8.3
2.8
Epilepsy duration
fcd
arachnoid cyst+hippocampal sclerosis
hippocampal sclerosis
inflammation
microgyria/fcd
fcd
fcd
Histology
free
free
free
not free
not free
free
free
Seizure Outcome
ES=epileptic spasms, SP=simple partial seizure, CP=complex partial seizure, GTC=generalized tonic-clonic seizure, Fr=Frontal, T=temporal, P=parietal, O=occipital, fcd=focal cortical dysplasia. Age and epilepsy duration are reported in years.
6.6
8.1
30
35
3.4
14.3
29
Type
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gender
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age
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index
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Table 2
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Results of permuted Spearman’s ρ-rank analyses between post-operative connectivity score changes and different types of regressors.
Regressor
regressor
insular–inferior frontal operculum
superior frontal orbitalmid frontal orbital
ρ-coefficient
0.3997
0.4021
p-value
0.0180*
0.0166*
ρ-coefficient
−0.3413
−0.3388
p-value
0.0448*
0.0465*
ρ-coefficient
0.2853
0.3441
p-value
0.2832
0.1919
ρ-coefficient
0.1807
0.2273
p-value
0.4575
0.3494
ρ-coefficient
−0.1455
0.2909
p-value
0.6734
0.3864
ρ-coefficient
0.5913
0.4475
p-value
0.0028*
0.0283*
correlation measure
resection volumes of all patients
ages of all patients
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post-operative connectivity score changes (i.e, postoperative scores – preoperative scores)
frontal resection volumes
extra-frontal resection volumes resection volumes of not seizurefree patients resection volumes of seizure-free patients
*
indicates p-value
