Structural brain network analysis of children with localization-related epilepsy

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

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 fromn $$ n $$ = 5251 pre/early adolescents in the adolescent brain cognitive development [ABCD] study), andn $$ n $$ = 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.

EEG based brain functional connectivity analysis for post-autoimmune encephalitis (AE) patients with epilepsy

Autoimmune Encephalitis (AE) refers to a group of conditions that occur when the body's immune system mistakenly attacks healthy brain cells, leading to inflammation of the brain. Seizures are a common symptom of AE and more than a third of patients experiencing seizures secondary to AE become epileptic over time. The objective of the present study is to identify biomarkers that can be used to identify those patients in whom AE will evolve into epilepsy. The bursts of abnormal electrical activity that occur during a seizure can be recorded by using Electroencephalography (EEG). In this work, common EEG (cEEG) and ambulatory EEG (aEEG) were recorded to compare the brain functional connectivity (FC) properties in post-AE patients with epilepsy patients and post-AE patients without epilepsy. The brain functional networks of spike waves were first constructed on the basis of Phase Locking Value (PLV). An analysis was then performed of the differences which existed in the FC properties of clustering coefficient, characteristic path length, global efficiency, local efficiency, and node degree between post-AE patients with epilepsy patients and post-AE patients without epilepsy. From the perspective of brain functional network analysis, post-AE patients with epilepsy showed a more complex network structure. Furthermore, the five FC properties have been found signification different, all FC property values of post-AE patients with epilepsy are higher than those of post-AE patients without epilepsy of cEEG and aEEG. Based on the extracted FC properties, five classifiers were used to classify them, and the results showed that all five FC properties could effectively distinguish between post-AE patients with epilepsy patients and post-AE patients without epilepsy in both cEEG and aEEG. These findings are potentially helpful for diagnosing whether a patient with AE will become epileptic.

Social brain networks: Resting-state and task-based connectivity in youth with and without epilepsy

Individuals with epilepsy often experience social difficulties and deficits in social cognition. It remains unknown how disruptions to neural networks underlying such skills may contribute to this clinical phenotype. The current study compared the organization of relevant brain circuits-the "mentalizing network" and a salience-related network centered on the amygdala-in youth with and without epilepsy. Functional connectivity between the nodes of these networks was assessed, both at rest and during engagement in a social cognitive task (facial emotion recognition), using functional magnetic resonance imaging. There were no group differences in resting-state connectivity within either neural network. In contrast, youth with epilepsy showed comparatively lower connectivity between the left posterior superior temporal sulcus and the medial prefrontal cortex-but greater connectivity within the left temporal lobe-when viewing faces in the task. These findings suggest that the organization of a mentalizing network underpinning social cognition may be disrupted in youth with epilepsy, though differences in connectivity within this circuit may shift depending on task demands. Our results highlight the importance of considering functional task-based engagement of neural systems in characterizations of network dysfunction in epilepsy.

Improvement of brain function after surgery in infants with posterior quadrant cortical dysplasia

OBJECTIVE

To reveal whether neurodevelopmental outcome of infants after epilepsy surgery can be quantitatively assessed by electroencephalography (EEG) functional connectivity analysis.

METHODS

We enrolled 13 infants with posterior quadrant dysplasia aged <2 years who were treated using posterior quadrantectomy and 21 age-matched infants. EEG was performed both before and one year after surgery. Developmental quotient (DQ) was assessed both before and 3 years after surgery. The phase lag index (PLI) of three different pairs of electrodes in the nonsurgical hemisphere, i.e., the anterior short distance (ASD), posterior short distance (PSD), and long distance (LD) pairs, were calculated as indices of brain connectivity. The relationship between the PLI and DQ was evaluated.

RESULTS

Overall, 77% infants experienced seizure freedom after surgery. The beta- and gamma- range PLI of PSD pairs increased preoperatively. All these pairs normalized postoperatively. Simple linear regression analysis revealed a significant relationship between the postoperative DQ and the postoperative beta-band PLI of ASD pairs.

CONCLUSION

Preoperative abnormal hyper-connectivity was normalized to the control level after surgery. The postoperative hyperconnectivity was associated with long-term neurodevelopmental improvement.

SIGNIFICANCE

PLI quantifies neurodevelopmental improvements after posterior quadrantectomy.

Longitudinal analysis of structural connectivity in patients with newly diagnosed focal epilepsy of unknown origin

OBJECTIVES

The aim of this longitudinal study was to clarify whether significant alterations in structural connectivity occur over time in patients with newly diagnosed focal epilepsy of unknown origin.

METHODS

A total of 40 patients with newly diagnosed focal epilepsy of unknown origin and with normal brain magnetic resonance imaging (MRI) on visual inspection were enrolled. All subjects underwent MRI twice involving three-dimensional volumetric T1-weighted imaging, which were suitable for structural volume analysis. Gray matter volumes were obtained using the FreeSurfer image analysis suite, and structural connectivity analyses were performed using Matlab-based BRain Analysis using graPH theory software.

RESULTS

The median interval between the two MRI scans was 18.5 months in patients with epilepsy. There was a general tendency toward decreased gray matter volumes on the second scan compared with the initial scan. However, the volumes of the right and left thalamus and brainstem on the second MRI scan had an increased tendency compared with those on the initial MRI scan. In measures of connectivity, there were significant differences between the two MRI scans. The mean clustering coefficient, global efficiency, local efficiency, and the small-worldness index were significantly increased, whereas the characteristic path length was decreased on the second MRI scan compared with the initial MRI scan.

CONCLUSIONS

The structural connectivity in patients with newly diagnosed focal epilepsy of unknown origin increases over time in the initial stage. These alterations and increases in structural connectivity may be related to underlying epileptogenicity in the initial stages of epilepsy.

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

PURPOSE

This 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 METHODS

We 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.

RESULTS

Comparisons 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).

CONCLUSION

Graph 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.

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

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₎ = -2.0; P = .04] and social function [t₍₄₀₎ = -2.9; P = .005]. There was a positive association between the mean participation coefficient and total Quality of Life in Childhood Epilepsy score [t₍₄₀₎ = 2.2; P = .03] and cognition [t₍₄₀₎ = 3.8; P = .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₎ = -2.8; P = .02] and social function [t₍₈₎ = -3.6; P = .0075] and between modularity and total Quality of Life in Childhood Epilepsy score [t₍₈₎ = -2.5; P = .04] and social function [t₍₈₎ = -4.4; P = .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.

Identifying Factors Associated With Severe Intellectual Disabilities in Teenagers With Cerebral Palsy Using a Predictive Learning Model

BACKGROUND

Intellectual disability and impaired adaptive functioning are common in children with cerebral palsy, but there is a lack of studies assessing these issues in teenagers with cerebral palsy. Therefore, the aim of this study was to develop and test a predictive machine learning model to identify factors associated with intellectual disability in teenagers with cerebral palsy.

METHODS

This was a multicenter controlled cohort study of 91 teenagers with cerebral palsy (53 males, 38 females; mean age ± SD = 17 ± 1 y; range: 12-18 y). Data on etiology, diagnosis, spasticity, epilepsy, clinical history, communication abilities, behaviors, motor skills, eating, and drinking abilities were collected between 2005 and 2015. Intellectual disability was classified as "mild," "moderate," "severe," or "profound" based on adaptive functioning, and according to the DSM-5 after 2013 and DSM-IV before 2013, the Wechsler Intelligence Scale for Children for patients up to ages 16 years, 11 months, and the Wechsler Adult Intelligence Scale for patients ages 17-18. Statistical analysis included Fisher's exact test and multiple logistic regressions to identify factors associated with intellectual disability. A predictive machine learning model was developed to identify factors associated with having profound intellectual disability. The guidelines of the "Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis Statement" were followed.

RESULTS

Poor manual abilities (P ≤ .001), gross motor function (P ≤ .001), and type of epilepsy (intractable: P = .04; well controlled: P = .01) were significantly associated with profound intellectual disability. The average model accuracy, specificity, and sensitivity was 78%.

CONCLUSION

Poor motor skills and epilepsy were associated with profound intellectual disability. The machine learning prediction model was able to adequately identify high likelihood of severe intellectual disability in teenagers with cerebral palsy.

Reduced resilience of brain gray matter networks in idiopathic generalized epilepsy: A graph-theoretical analysis

Purpose

The pathophysiology of idiopathic generalized epilepsy (IGE) is still unclear, but graph theory may help to understand it. Here, we examined the graph-theoretical findings of the gray matter network in IGE using anatomical covariance methods.

Materials and methods

We recruited 33 patients with IGE and 35 age- and sex-matched healthy controls. Gray matter images were obtained by 3.0-T 3D T1-weighted MRI and were normalized using the voxel-based morphometry tools of Statistical Parametric Mapping 12. The normalized images were subjected to graph-theoretical group comparison using the Graph Analysis Toolbox with two different parcellation schemes. Initially, we used the Automated Anatomical Labeling template, whereas the Hammers Adult atlas was used for the second analysis.

Results

The resilience analyses revealed significantly reduced resilience of the IGE gray matter networks to both random failure and targeted attack. No significant between-group differences were found in global network measures, including the clustering coefficient and characteristic path length. The IGE group showed several changes in regional clustering, including an increase mainly in wide areas of the bilateral frontal lobes. The second analysis with another region of interest (ROI) parcellation generated the same results in resilience and global network measures, but the regional clustering results differed between the two parcellation schemes.

Conclusion

These results may reflect the potentially weak network organization in IGE. Our findings contribute to the accumulation of knowledge on IGE.