Structural brain network analysis of children with localization-related epilepsy

Takeda, Kanako; Miyamoto, Yusaku; Yamamoto, Hitoshi

doi:10.1016/j.braindev.2017.04.010

Cited by 9 publications

(7 citation statements)

References 37 publications

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“…3 A high clustering coefficient indicates that the neighbors of a node are directly connected to each other. 26 An increased clustering coefficient has been reported in the bilateral cingulate, right peri-Sylvian, and medial frontal lobes in children with focal epilepsy relative to controls, 27 in the polymicrogyria cortex relative to the normal cortex, 28 and in the affected hippocampus and anterior thalamus in patients with hippocampal sclerosis. 29 Participation coefficient measures the connectedness of a within-module node to a diverse set of segregated modules and hence the diversity of intermodular connections, which reflects network integration.…”

Section: Discussionmentioning

confidence: 98%

Functional Connectivity Associated with Health-Related Quality of Life in Children with Focal Epilepsy

Nawani¹,

Smith²,

Wheeler

et al. 2019

AJNR Am J Neuroradiol

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BACKGROUND AND PURPOSE: Although functional connectivity has been linked to cognitive function in epilepsy, its relationship with physical, psychological, or social dysfunction is unknown. This study aimed to assess the relationship between network architecture from resting-state fMRI and health-related quality of life in children with medically intractable focal epilepsy. MATERIALS AND METHODS: Forty-seven children with nonlesional focal epilepsy were included; 22 had frontal lobe epilepsy and 15 had temporal lobe epilepsy. We computed graph metrics of functional connectivity, including network segregation (clustering coefficient and modularity) and integration (characteristic path length and participation coefficient). Health-related quality of life was measured using the Quality of Life in Childhood Epilepsy questionnaire. We examined the associations between graph metrics and the Quality of Life in Childhood Epilepsy total and domains scores, with age, sex, age at seizure onset, fMRI motion, and network density as covariates. RESULTS: There was a negative relationship between the clustering coefficient and total Quality of Life in Childhood Epilepsy score [t (40) ϭ Ϫ2.0; P ϭ .04] and social function [t (40) ϭ Ϫ2.9; P ϭ .005]. There was a positive association between the mean participation coefficient and total Quality of Life in Childhood Epilepsy score [t (40) ϭ 2.2; P ϭ .03] and cognition [t (40) ϭ 3.8; P ϭ .0004]. In temporal lobe epilepsy, there was a negative relationship between the clustering coefficient and total Quality of Life in Childhood Epilepsy score [t (8) ϭ Ϫ2.8; P ϭ .02] and social function [t (8) ϭ Ϫ3.6; P ϭ .0075] and between modularity and total Quality of Life in Childhood Epilepsy score [t (8) ϭ Ϫ2.5; P ϭ .04] and social function [t (8) ϭ Ϫ4.4; P ϭ .0021]. In frontal lobe epilepsy, there was no association between network segregation and integration and Quality of Life in Childhood Epilepsy total or domain scores. CONCLUSIONS: Our findings indicate that there are other higher order brain functions beyond cognition, which may be linked with functional connectivity of the brain. ABBREVIATIONS: FLE ϭ frontal lobe epilepsy; rsfMRI ϭ resting-state fMRI; QOLCE ϭ Quality of Life in Childhood Epilepsy; TLE ϭ temporal lobe epilepsy

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Section: Discussionmentioning

confidence: 98%

Functional Connectivity Associated with Health-Related Quality of Life in Children with Focal Epilepsy

Nawani¹,

Smith²,

Wheeler

et al. 2019

AJNR Am J Neuroradiol

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“…No change in global network efficiency among the three groups was found in the present epilepsy patients. Previous studies taking graph theoretical approaches contrarily revealed an impaired anatomical covariance network affecting global networks in patients with generalized or focal epilepsy and stronger structural global connectivity and modularity with excessive network robustness versus controls [15, 19]. Fewer edges, weaker clustering coefficients, and longer path lengths in structural networks constructed using DTI are related to lower cognitive performance in adult patients with chronic epilepsy [24].…”

Section: Discussionmentioning

confidence: 99%

“…In addition, 32 age-matched patients with no central nervous system abnormality were recruited to comprise the control group in a conventional manner [19, 20]. The most common diagnosis in control patients was orthostatic hypotension (10 patients).…”

Section: Methodsmentioning

confidence: 99%

“…The framework of the analysis is the same as in Takeda et al [19] and Sone et al [21]. Brain MRI was acquired twice in the patients—preoperatively (pre-CC data group) and postoperatively (mean 4.5 months after CC, post-CC data group)—and once in the control group using a 3.0-T MR system with a 32-channel coil (Philips Medical Systems, Best, The Netherlands).…”

Section: Methodsmentioning

confidence: 99%

“…The segmented gray and white matter images were spatially normalized to a customized template in standardized anatomic space using Diffeomorphic Anatomical Registration through Exponentiated Lie (DARTEL) [22]. Each image was modulated by the Jacobian determinant derived from the spatial normalization by DARTEL and spatially smoothed with an 8-mm full-width at half-maximum Gaussian kernel to decrease spatial noise and compensate for imprecise normalization [19, 21]. The morphological differences between the pre-CC and post-CC patient data groups and between the pre-CC and control groups were confirmed by submitting the resulting gray matter images after normalization using the Computational Anatomy Toolbox (CAT12) (http://www.neuro.uni-jena.de/cat/), which runs within SPM12.…”

Section: Methodsmentioning

confidence: 99%

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Alteration of the anatomical covariance network after corpus callosotomy in pediatric intractable epilepsy

et al. 2019

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PurposeThis study aimed to use graph theoretical analysis of anatomical covariance derived from structural MRI to reveal how the gray matter connectivity pattern is altered after corpus callosotomy (CC).Materials and methodsWe recruited 21 patients with epilepsy who had undergone CC. Enrollment criteria were applied: (1) no lesion identified on brain MRI; (2) no history of other brain surgery; and (3) age not younger than 3 years and not older than 18 years at preoperative MRI evaluation. The most common epilepsy syndrome was Lennox-Gastaut syndrome (11 patients). For voxel-based morphometry, the normalized gray matter images of pre-CC and post-CC patients were analyzed with SPM12 (voxel-level threshold of p<0.05 [familywise error-corrected]). Secondly, the images of both groups were subjected to graph theoretical analysis using the Graph Analysis Toolbox with SPM8. Each group was also compared with 32 age- and sex-matched control patients without brain diseases.ResultsComparisons between the pre- and post-CC groups revealed a significant reduction in seizure frequency with no change in mean intelligence quotient/developmental quotient levels. There was no relationship among the three groups in global network metrics or in targeted attack. A regional comparison of betweenness centrality revealed decreased connectivity to and from the right middle cingulate gyri and medial side of the right superior frontal gyrus and a partial shift in the distribution of betweenness centrality hubs to the normal location. Significantly lower resilience to random failure was found after versus before CC and versus controls (p = 0.0450 and p = 0.0200, respectively).ConclusionGraph theoretical analysis of anatomical covariance derived from structural imaging revealed two neural network effects of resection associated with seizure reduction: the reappearance of a structural network comparable to that in healthy children and reduced connectivity along the median line, including the middle cingulate gyrus.

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Community detection in the human connectome: Method types, differences and their impact on inference

Brooks,

Jones,

Wang

et al. 2024

Human Brain Mapping

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Community structure is a fundamental topological characteristic of optimally organized brain networks. Currently, there is no clear standard or systematic approach for selecting the most appropriate community detection method. Furthermore, the impact of method choice on the accuracy and robustness of estimated communities (and network modularity), as well as method‐dependent relationships between network communities and cognitive and other individual measures, are not well understood. This study analyzed large datasets of real brain networks (estimated from resting‐state fMRI from = 5251 pre/early adolescents in the adolescent brain cognitive development [ABCD] study), and = 5338 synthetic networks with heterogeneous, data‐inspired topologies, with the goal to investigate and compare three classes of community detection methods: (i) modularity maximization‐based (Newman and Louvain), (ii) probabilistic (Bayesian inference within the framework of stochastic block modeling (SBM)), and (iii) geometric (based on graph Ricci flow). Extensive comparisons between methods and their individual accuracy (relative to the ground truth in synthetic networks), and reliability (when applied to multiple fMRI runs from the same brains) suggest that the underlying brain network topology plays a critical role in the accuracy, reliability and agreement of community detection methods. Consistent method (dis)similarities, and their correlations with topological properties, were estimated across fMRI runs. Based on synthetic graphs, most methods performed similarly and had comparable high accuracy only in some topological regimes, specifically those corresponding to developed connectomes with at least quasi‐optimal community organization. In contrast, in densely and/or weakly connected networks with difficult to detect communities, the methods yielded highly dissimilar results, with Bayesian inference within SBM having significantly higher accuracy compared to all others. Associations between method‐specific modularity and demographic, anthropometric, physiological and cognitive parameters showed mostly method invariance but some method dependence as well. Although method sensitivity to different levels of community structure may in part explain method‐dependent associations between modularity estimates and parameters of interest, method dependence also highlights potential issues of reliability and reproducibility. These findings suggest that a probabilistic approach, such as Bayesian inference in the framework of SBM, may provide consistently reliable estimates of community structure across network topologies. In addition, to maximize robustness of biological inferences, identified network communities and their cognitive, behavioral and other correlates should be confirmed with multiple reliable detection methods.

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Structural brain network analysis of children with localization-related epilepsy

Cited by 9 publications

References 37 publications

Functional Connectivity Associated with Health-Related Quality of Life in Children with Focal Epilepsy

Functional Connectivity Associated with Health-Related Quality of Life in Children with Focal Epilepsy

Alteration of the anatomical covariance network after corpus callosotomy in pediatric intractable epilepsy

Community detection in the human connectome: Method types, differences and their impact on inference

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