Metrics of brain network architecture capture the impact of disease in children with epilepsy

High modularity, more flexible of brain networks in patients with mild to moderate motor impairments after stroke

Stroke is recognized as a network communication disorder. Advances in neuroimaging technologies have enhanced our comprehension of dynamic cerebral alterations. However, different levels of motor function impairment after stroke may have different patterns of brain reorganization. Abnormal and adaptive patterns of brain activity in mild-to-moderate motor function impairments after stroke remain still underexplored. We aim to identify dynamic patterns of network remodeling in stroke patients with mild-to-moderate impairment of motor function. fMRI data were obtained from 30 stroke patients and 31 healthy controls to establish a spatiotemporal multilayer modularity model. Then, graph-theoretic measures, including modularity, flexibility, cohesion, and disjointedness, were calculated to quantify dynamic reconfiguration. Our findings reveal that the post-stroke brain exhibited higher modular organization, as well as heightened disjointedness, compared to HCs. Moreover, analyzing from the network level, we found increased disjointedness and flexibility in the Default mode network (DMN), indicating that brain regions tend to switch more frequently and independently between communities and the dynamic changes were mainly driven by DMN. Notably, modified functional dynamics positively correlated with motor performance in patients with mild-to-moderate motor impairment. Collectively, our research uncovered patterns of dynamic community reconstruction in multilayer networks following stroke. Our findings may offer new insights into the complex reorganization of neural function in post-stroke brain.

Cerebral cortical functional hyperconnectivity in a mouse model of spinocerebellar ataxia type 8 (SCA8)

Spinocerebellar Ataxia Type 8 (SCA8) is an inherited neurodegenerative disease caused by a bidirectionally expressed CTG●CAG expansion mutation in the ATXN-8 and ATXN8-OS genes. While primarily a motor disorder, psychiatric and cognitive symptoms have been reported. It is difficult to elucidate how the disease alters brain function in areas with little or no degeneration producing both motor and cognitive symptoms. Using transparent polymer skulls and CNS-wide GCaMP6f expression, we studied neocortical networks throughout SCA8 progression using wide-field Ca2+ imaging in a transgenic mouse model of SCA8. We observed that neocortical networks in SCA8+ mice were hyperconnected globally which led to network configurations with increased global efficiency and centrality. At the regional level, significant network changes occurred in nearly all cortical regions, however mainly involved sensory and association cortices. Changes in functional connectivity in anterior motor regions worsened later in the disease. Near perfect decoding of animal genotype was obtained using a generalized linear model based on canonical correlation strengths between activity in cortical regions. The major contributors to decoding were concentrated in the somatosensory, higher visual and retrosplenial cortices and occasionally extended into the motor regions, demonstrating that the areas with the largest network changes are predictive of disease state.

Functional brain connectivity in children with focal epilepsy: A systematic review of functional MRI studies

Epilepsy is increasingly recognised as a brain network disorder and many studies have investigated functional connectivity (FC) in children with epilepsy using functional MRI (fMRI). This systematic review of fMRI studies, published up to November 2023, investigated profiles of FC changes and their clinical relevance in children with focal epilepsy compared to healthy controls. A literature search in PubMed and Web of Science yielded 62 articles. We categorised the results into three groups: 1) differences in correlation-based FC between patients and controls; 2) differences in other FC measures between patients and controls; and 3) associations between FC and disease variables (for example, age of onset), cognitive and seizure outcomes. Studies revealed either increased or decreased FC across multiple brain regions in children with focal epilepsy. However, findings lacked consistency: conflicting FC alterations (decreased and increased FC) co-existed within or between brain regions across all focal epilepsy groups. The studies demonstrated overall that 1) interhemispheric connections often displayed abnormal connectivity and 2) connectivity within and between canonical functional networks was decreased, particularly for the default mode network. Focal epilepsy disrupted FC in children both locally (e.g., seizure-onset zones, or within-brain subnetworks) and globally (e.g., whole-brain network architecture). The wide variety of FC study methodologies limits clinical application of the results. Future research should employ longitudinal designs to understand the evolution of brain networks during the disease course and explore the potential of FC biomarkers for predicting cognitive and postsurgical seizure outcomes.

Multilayer brain network modeling and dynamic analysis of juvenile myoclonic epilepsy

Objective: It is indisputable that the functional connectivity of the brain network in juvenile myoclonic epilepsy (JME) patients is abnormal. As a mathematical extension of the traditional network model, the multilayer network can fully capture the fluctuations of brain imaging data with time, and capture subtle abnormal dynamic changes. This study assumed that the dynamic structure of JME patients is abnormal and used the multilayer network framework to analyze the change brain community structure in JME patients from the perspective of dynamic analysis.

Methods: First, functional magnetic resonance imaging (fMRI) data were obtained from 35 JME patients and 34 healthy control subjects. In addition, the communities of the two groups were explored with the help of a multilayer network model and a multilayer community detection algorithm. Finally, differences were described by metrics that are specific to the multilayer network.

Results: Compared with healthy controls, JME patients had a significantly lower modularity degree of the brain network. Furthermore, from the level of the functional network, the integration of the default mode network (DMN) and visual network (VN) in JME patients showed a significantly higher trend, and the flexibility of the attention network (AN) also increased significantly. At the node level, the integration of seven nodes of the DMN was significantly increased, the integration of five nodes of the VN was significantly increased, and the flexibility of three nodes of the AN was significantly increased. Moreover, through division of the core-peripheral system, we found that the left insula and left cuneus were core regions specific to the JME group, while most of the peripheral systems specific to the JME group were distributed in the prefrontal cortex and hippocampus. Finally, we found that the flexibility of the opercular part of the inferior frontal gyrus was significantly correlated with the severity of JME symptoms.

Conclusion: Our findings indicate that the dynamic community structure of JME patients is indeed abnormal. These results provide a new perspective for the study of dynamic changes in communities in JME patients.

Neuropsychological Phenotypes in Pediatric Temporal Lobe Epilepsy

OBJECTIVE

Adults with temporal lobe epilepsy (TLE) have been found to have a fairly characteristic pattern of neuropsychological performance, but there is considerably less research and more variability in findings with children. Because the cognitive domains included in most studies with children have been limited, the current study attempted to better characterize the cognitive phenotype of children with TLE using a broader neuropsychological battery.

METHODS

The study included 59 children with TLE (59% male) age 7 to 16 (M = 12.67; SD = 3.12) who underwent comprehensive neuropsychological evaluation. Patient results were grouped into cognitive domains (reasoning, language, visuoperceptual, verbal memory, executive function, and motor function) based upon their test performance. These factor scores were subjected to Ward's hierarchical clustering method with squared Euclidean distance.

RESULTS

Cluster analysis revealed three distinct cognitive profiles: (1) normal functioning (20% of sample); (2) delayed verbal memory and motor weaknesses (61% of the sample); and (3) global impairment (19% of the sample). Cluster 3 had longer epilepsy duration and a higher incidence of hippocampal sclerosis (HS) compared to Cluster 1 (p < .05). There were no significant differences among the three cluster groups on demographic characteristics or remaining clinical characteristics.

CONCLUSIONS

Children with TLE present with distinct cognitive phenotypes ranging from average performance to global impairment. Results partially support previous hypotheses highlighting the cumulative neurobiological burden on the developing brain in the context of chronic epilepsy and provide a preliminary framework for the cognitive domains most vulnerable to the TLE disease process.

Altered topological organization of resting-state functional networks in children with infantile spasms

Covering neuroimaging evidence has demonstrated that epileptic symptoms are associated with the disrupted topological architecture of the brain network. Infantile spasms (IS) as an age-specific epileptic encephalopathy also showed abnormal structural or functional connectivity in specific brain regions or specific networks. However, little is known about the topological alterations of whole-brain functional networks in patients with IS. To fill this gap, we used the graph theoretical analysis to investigate the topological properties (whole-brain small-world property and modular interaction) in 17 patients with IS and 34 age- and gender-matched healthy controls. The functional networks in both groups showed efficient small-world architecture over the sparsity range from 0.05 to 0.4. While patients with IS showed abnormal global properties characterized by significantly decreased normalized clustering coefficient, normalized path length, small-worldness, local efficiency, and significantly increased global efficiency, implying a shift toward a randomized network. Modular analysis revealed decreased intra-modular connectivity within the default mode network (DMN) and fronto-parietal network but increased inter-modular connectivity between the cingulo-opercular network and occipital network. Moreover, the decreased intra-modular connectivity in DMN was significantly negatively correlated with seizure frequency. The inter-modular connectivity between the cingulo-opercular and occipital network also showed a significant correlation with epilepsy frequency. Together, the current study revealed the disrupted topological organization of the whole-brain functional network, which greatly advances our understanding of neuronal architecture in IS and may contribute to predict the prognosis of IS as disease biomarkers.

10-year follow-up study found that motor-free intelligence quotient declined in children with mild to moderate cerebral palsy

Aim

This 10‐year follow‐up study examined cognitive change in a cohort of children with cerebral palsy from preschool to adolescence at the group and individual levels.

Methods

The Wechsler Preschool and Primary Scale of Intelligence was administered to 80 children with cerebral palsy (mean = 4 years 6 months, standard deviation = 7 months) at baseline (Time 1). At 10‐year follow‐up (Time 2), 28 adolescents (mean = 14 years 6 months, standard deviation = 9 months) returned for assessment with the Wechsler Intelligence Scale for Children. Motor‐free intelligence quotient (IQ) scores were calculated and paired‐samples t‐tests and the Reliable Change Index (RCI) were used to investigate change in IQ over time.

Results

At the group level, nonverbal IQ scores declined significantly. At the individual level, RCI indicated nine and 11 children showed a clinically significant decline in Full Scale IQ (FSIQ) and nonverbal IQ scores, respectively. Decline in FSIQ was related to a history of seizures whereas decline in nonverbal IQ was associated with higher initial IQ.

Conclusion

Cognitive abilities in children with cerebral palsy evolve over time and selective deficits may not be observable until a later age, highlighting the importance of repeated cognitive assessment throughout childhood and adolescence.

Contralesional Sensorimotor Network Participates in Motor Functional Compensation in Glioma Patients

Background

Some gliomas in sensorimotor areas induce motor deficits, while some do not. Cortical destruction and reorganization contribute to this phenomenon, but detailed reasons remain unclear. This study investigated the differences of the functional connectivity and topological properties in the contralesional sensorimotor network (cSMN) between patients with motor deficit and those with normal motor function.

Methods

We retrospectively reviewed 65 patients (32 men) between 2017 and 2020. The patients were divided into four groups based on tumor laterality and preoperative motor status (deficit or non-deficit). Thirty-three healthy controls (18 men) were enrolled after matching for sex, age, and educational status. Graph theoretical measurement was applied to reveal alterations of the topological properties of the cSMN by analyzing resting-state functional MRI.

Results

The results for patients with different hemispheric gliomas were similar. The clustering coefficient, local efficiency, transitivity, and vulnerability of the cSMN significantly increased in the non-deficit group and decreased in the deficit group compared to the healthy group (p < 0.05). Moreover, the nodes of the motor-related thalamus showed a significantly increased nodal efficiency and nodal local efficiency in the non-deficit group and decreased in the deficit group compared with the healthy group (p < 0.05).

Conclusions

We posited the existence of two stages of alterations of the preoperative motor status. In the compensatory stage, the cSMN sacrificed stability to acquire high efficiency and to compensate for impaired motor function. With the glioma growing and the motor function being totally damaged, the cSMN returned to a stable state and maintained healthy hemispheric motor function, but with low efficiency.

Individual-specific networks for prediction modelling – A scoping review of methods

Background

Recent advances in biotechnology enable the acquisition of high-dimensional data on individuals, posing challenges for prediction models which traditionally use covariates such as clinical patient characteristics. Alternative forms of covariate representations for the features derived from these modern data modalities should be considered that can utilize their intrinsic interconnection. The connectivity information between these features can be represented as an individual-specific network defined by a set of nodes and edges, the strength of which can vary from individual to individual. Global or local graph-theoretical features describing the network may constitute potential prognostic biomarkers instead of or in addition to traditional covariates and may replace the often unsuccessful search for individual biomarkers in a high-dimensional predictor space.

Methods

We conducted a scoping review to identify, collate and critically appraise the state-of-art in the use of individual-specific networks for prediction modelling in medicine and applied health research, published during 2000–2020 in the electronic databases PubMed, Scopus and Embase.

Results

Our scoping review revealed the main application areas namely neurology and pathopsychology, followed by cancer research, cardiology and pathology (N = 148). Network construction was mainly based on Pearson correlation coefficients of repeated measurements, but also alternative approaches (e.g. partial correlation, visibility graphs) were found. For covariates measured only once per individual, network construction was mostly based on quantifying an individual’s contribution to the overall group-level structure. Despite the multitude of identified methodological approaches for individual-specific network inference, the number of studies that were intended to enable the prediction of clinical outcomes for future individuals was quite limited, and most of the models served as proof of concept that network characteristics can in principle be useful for prediction.

Conclusion

The current body of research clearly demonstrates the value of individual-specific network analysis for prediction modelling, but it has not yet been considered as a general tool outside the current areas of application. More methodological research is still needed on well-founded strategies for network inference, especially on adequate network sparsification and outcome-guided graph-theoretical feature extraction and selection, and on how networks can be exploited efficiently for prediction modelling.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12874-022-01544-6.

The Effect of Aging on Brain Glucose Metabolic Connectivity Revealed by [18F]FDG PET-MR and Individual Brain Networks

Contrary to group-based brain connectivity analyses, the aim of this study was to construct individual brain metabolic networks to determine age-related effects on brain metabolic connectivity. Static 40–60 min [¹⁸F]FDG positron emission tomography (PET) images of 67 healthy subjects between 20 and 82 years were acquired with an integrated PET-MR system. Network nodes were defined by brain parcellation using the Schaefer atlas, while connectivity strength between two nodes was determined by comparing the distribution of PET uptake values within each node using a Kullback–Leibler divergence similarity estimation (KLSE). After constructing individual brain networks, a linear and quadratic regression analysis of metabolic connectivity strengths within- and between-networks was performed to model age-dependency. In addition, the age dependency of metrics for network integration (characteristic path length), segregation (clustering coefficient and local efficiency), and centrality (number of hubs) was assessed within the whole brain and within predefined functional subnetworks. Overall, a decrease of metabolic connectivity strength with healthy aging was found within the whole-brain network and several subnetworks except within the somatomotor, limbic, and visual network. The same decrease of metabolic connectivity was found between several networks across the whole-brain network and the functional subnetworks. In terms of network topology, a less integrated and less segregated network was observed with aging, while the distribution and the number of hubs did not change with aging, suggesting that brain metabolic networks are not reorganized during the adult lifespan. In conclusion, using an individual brain metabolic network approach, a decrease in metabolic connectivity strength was observed with healthy aging, both within the whole brain and within several predefined networks. These findings can be used in a diagnostic setting to differentiate between age-related changes in brain metabolic connectivity strength and changes caused by early development of neurodegeneration.

Artificial Intelligence Applications in the Imaging of Epilepsy and Its Comorbidities: Present and Future

Artificial intelligence (AI) is increasingly used in medical image analysis and has accelerated scientific discoveries across fields of medicine. In this review, we highlight how AI has been applied to neuroimaging in patients with epilepsy to enhance classification of clinical diagnosis, prediction of treatment outcomes, and the understanding of cognitive comorbidities. We outline the strengths and shortcomings of current AI research and the need for future studies using large datasets that test the reproducibility and generalizability of current findings, as well as studies that test the clinical utility of AI approaches.

An updated systematic review and meta-analysis of brain network organization in focal epilepsy: Looking back and forth

Abnormalities of the brain network organization in focal epilepsy have been extensively quantified. However, the extent and directionality of abnormalities are highly variable and subtype insensitive. We conducted meta-analyses to obtain a more accurate and epilepsy type-specific quantification of the interictal global brain network organization in focal epilepsy. By using random-effects models, we estimated differences in average clustering coefficient, average path length, and modularity between patients with focal epilepsy and controls, based on 45 studies with a total sample size of 1,468 patients and 1,021 controls. Structural networks had a significant lower level of integration in patients with epilepsy as compared to controls, with a standardized mean difference of -0.334 (95 % confidence interval -0.631 to -0.038; p-value 0.027). Functional networks did not differ between patients and controls, except for the beta band clustering coefficient. Our meta-analyses show that differences in the brain network organization are not as well defined as individual studies often propose. We discuss potential pitfalls and suggestions to enhance the yield and clinical value of network studies.

The gut microbiota modulates brain network connectivity under physiological conditions and after acute brain ischemia

The gut microbiome has been implicated as a key regulator of brain function in health and disease. But the impact of gut microbiota on functional brain connectivity is unknown. We used resting-state functional magnetic resonance imaging in germ-free and normally colonized mice under naive conditions and after ischemic stroke. We observed a strong, brain-wide increase of functional connectivity in germ-free animals. Graph theoretical analysis revealed significant higher values in germ-free animals, indicating a stronger and denser global network but with less structural organization. Breakdown of network function after stroke equally affected germ-free and colonized mice. Results from histological analyses showed changes in dendritic spine densities, as well as an immature microglial phenotype, indicating impaired microglia-neuron interaction in germ-free mice as potential cause of this phenomenon. These results demonstrate the substantial impact of bacterial colonization on brain-wide function and extend our so far mainly (sub) cellular understanding of the gut-brain axis.

Dopamine Transporter Is a Master Regulator of Dopaminergic Neural Network Connectivity

Dopaminergic neurons of the substantia nigra pars compacta (SNC) and ventral tegmental area (VTA) exhibit spontaneous firing activity. The dopaminergic neurons in these regions have been shown to exhibit differential sensitivity to neuronal loss and psychostimulants targeting dopamine transporter. However, it remains unclear whether these regional differences scale beyond individual neuronal activity to regional neuronal networks. Here, we used live-cell calcium imaging to show that network connectivity greatly differs between SNC and VTA regions with higher incidence of hub-like neurons in the VTA. Specifically, the frequency of hub-like neurons was significantly lower in SNC than in the adjacent VTA, consistent with the interpretation of a lower network resilience to SNC neuronal loss. We tested this hypothesis, in DAT-cre/loxP-GCaMP6f mice of either sex, when activity of an individual dopaminergic neuron is suppressed, through whole-cell patch clamp electrophysiology, in either SNC or VTA networks. Neuronal loss in the SNC increased network clustering, whereas the larger number of hub-neurons in the VTA overcompensated by decreasing network clustering in the VTA. We further show that network properties are regulatable via a dopamine transporter but not a D2 receptor dependent mechanism. Our results demonstrate novel regulatory mechanisms of functional network topology in dopaminergic brain regions.SIGNIFICANCE STATEMENT In this work, we begin to untangle the differences in complex network properties between the substantia nigra pars compacta (SNC) and VTA, that may underlie differential sensitivity between regions. The methods and analysis employed provide a springboard for investigations of network topology in multiple deep brain structures and disorders.

Clinical Application of Machine Learning Models for Brain Imaging in Epilepsy: A Review

Epilepsy is a common neurological disorder characterized by recurrent and disabling seizures. An increasing number of clinical and experimental applications of machine learning (ML) methods for epilepsy and other neurological and psychiatric disorders are available. ML methods have the potential to provide a reliable and optimal performance for clinical diagnoses, prediction, and personalized medicine by using mathematical algorithms and computational approaches. There are now several applications of ML for epilepsy, including neuroimaging analyses. For precise and reliable clinical applications in epilepsy and neuroimaging, the diverse ML methodologies should be examined and validated. We review the clinical applications of ML models for brain imaging in epilepsy obtained from a PubMed database search in February 2021. We first present an overview of typical neuroimaging modalities and ML models used in the epilepsy studies and then focus on the existing applications of ML models for brain imaging in epilepsy based on the following clinical aspects: (i) distinguishing individuals with epilepsy from healthy controls, (ii) lateralization of the temporal lobe epilepsy focus, (iii) the identification of epileptogenic foci, (iv) the prediction of clinical outcomes, and (v) brain-age prediction. We address the practical problems and challenges described in the literature and suggest some future research directions.

Impact of Brain Functional Network Properties on Intelligence in Children and Adolescents with Focal Epilepsy: A Resting-state MRI Study

RATIONALE AND OBJECTIVE

Epilepsy is a common pediatric disease that often leads to cognitive and intellectual impairments. Here, we explore the reorganized functional networks in children and adolescents with focal epilepsy (CAFE) and analyze the relationship between network reorganization and intellectual deficits to reveal the underlying link between them.

MATERIALS AND METHODS

Fifty-four CAFE (6-16 years old; right-handed) and 42 well-matched healthy controls were recruited. Subjects underwent resting-state functional magnetic resonance imaging, and functional networks were analyzed by graph analysis. Intelligence testing (Wechsler Intelligence Scale for Children-Chinese revision) included measures for verbal IQ (VIQ), performance IQ, and full-scale IQ.

RESULTS

(1) In the CAFE compared with the healthy controls, (a) the local efficiency, clustering coefficient and standardized clustering coefficient were significantly decreased (p < 0.05); (b) the degree centrality and nodal efficiency of the left precentral gyrus (LPG) were significantly increased (p < 0.05, Bonferroni correction), and the nodal shortest path length was significantly decreased (p < 0.05, Bonferroni correction); and (c) functional connectivity of the LPG with the bilateral inferior frontal ventral gyrus, right lateral superior occipital gyrus, left middle occipital gyrus, bilateral superior parietal lobule, right anterior prefrontal cortex, and bilateral cerebellum was enhanced (p < 0.05,GRF correction), while functional connectivity with the bilateral superior temporal gyrus was decreased (p < 0.05, GRF correction). (2) The nodal shortest path length of the LPG in CAFE was associated with full-scale IQ, performance IQ, and VIQ, and local efficiency was associated with VIQ.

CONCLUSION

Our results showed that the middle LPG in CAFE undergoes network reorganization that positively influences intelligence. Differences in local efficiency of functional networks in children and early adolescents have a significant effect on intelligence.

Comparison of topological properties of functional brain networks with graph theory in temporal lobe epilepsy with different duration of disease

Background

Our study was performed to measure the alterations in topological properties of the functional brain network of temporal lobe epilepsy (TLE) at different durations, exploring the potential progression and neuropathophysiological mechanisms of TLE.

Methods

Fifty-eight subjects, including 17 TLE patients with a disease duration of ≤5 years (TLE-SD), 20 TLE patients with a disease duration of >5 years (TLE-LD), and 21 healthy controls firstly underwent the Attention Network Test (ANT) to assess the alertness function and received the resting-state functional magnetic resonance imaging (rs-fMRI). Next, a functional brain network was set up, and then the related graph of theoretical network analysis was conducted. Finally, the correlation between network property and the neuropsychological score was analyzed.

Results

The global and local efficiencies of functional brain networks in TLE-SD patients significantly decreased and tended toward random alterations. Also, the degree centrality (DC) and nodal efficiency (Ne) in right medial pre-frontal thalamus (mPFtha) and right rostral temporal thalamus (rTtha) of TLE-SD patients significantly reduced. Further analysis showed that alertness was positively associated with the characteristic path length but negatively related to the global and local efficiencies in TLE-SD patients; alertness was negatively related to the Ne of mPFtha in TLE-LD patients.

Conclusions

Our study showed that the functional brain network of TLE patients might undergo compensatory reorganization as the disease progresses, which provides useful insights into the progression and mechanism of TLE.

Localization of epileptic seizure focus by computerized analysis of fMRI recordings

By computerized analysis of cortical activity recorded via fMRI for pediatric epilepsy patients, we implement algorithmic localization of epileptic seizure focus within one of eight cortical lobes. Our innovative machine learning techniques involve intensive analysis of large matrices of mutual information coefficients between pairs of anatomically identified cortical regions. Drastic selection of pairs of regions with biologically significant inter-connectivity provides efficient inputs for our multi-layer perceptron (MLP) classifier. By imposing rigorous parameter parsimony to avoid overfitting, we construct a small-size MLP with very good percentages of successful classification.

Activation of Functional Brain Networks in Children With Psychogenic Non-epileptic Seizures

Objectives

Psychogenic non-epileptic seizures (PNES) have been hypothesized to emerge in the context of neural networks instability. To explore this hypothesis in children, we applied a graph theory approach to examine connectivity in neural networks in the resting-state EEG in 35 children with PNES, 31 children with other functional neurological symptoms (but no PNES), and 75 healthy controls.

Methods

The networks were extracted from Laplacian-transformed time series by a coherence connectivity estimation method.

Results

Children with PNES (vs. controls) showed widespread changes in network metrics: increased global efficiency (gamma and beta bands), increased local efficiency (gamma band), and increased modularity (gamma and alpha bands). Compared to controls, they also had higher levels of autonomic arousal (e.g., lower heart variability); more anxiety, depression, and stress on the Depression Anxiety and Stress Scales; and more adverse childhood experiences on the Early Life Stress Questionnaire. Increases in network metrics correlated with arousal. Children with other functional neurological symptoms (but no PNES) showed scattered and less pronounced changes in network metrics.

Conclusion

The results indicate that children with PNES present with increased activation of neural networks coupled with increased physiological arousal. While this shift in functional organization may confer a short-term adaptive advantage-one that facilitates neural communication and the child's capacity to respond self-protectively in the face of stressful life events-it may also have a significant biological cost. It may predispose the child's neural networks to periods of instability-presenting clinically as PNES-when the neural networks are faced with perturbations in energy flow or with additional demands.

The sensitivity of network statistics to incomplete electrode sampling on intracranial EEG

Network neuroscience applied to epilepsy holds promise to map pathological networks, localize seizure generators, and inform targeted interventions to control seizures. However, incomplete sampling of the epileptic brain because of sparse placement of intracranial electrodes may affect model results. In this study, we evaluate the sensitivity of several published network measures to incomplete spatial sampling and propose an algorithm using network subsampling to determine confidence in model results. We retrospectively evaluated intracranial EEG data from 28 patients implanted with grid, strip, and depth electrodes during evaluation for epilepsy surgery. We recalculated global and local network metrics after randomly and systematically removing subsets of intracranial EEG electrode contacts. We found that sensitivity to incomplete sampling varied significantly across network metrics. This sensitivity was largely independent of whether seizure onset zone contacts were targeted or spared from removal. We present an algorithm using random subsampling to compute patient-specific confidence intervals for network localizations. Our findings highlight the difference in robustness between commonly used network metrics and provide tools to assess confidence in intracranial network localization. We present these techniques as an important step toward translating personalized network models of seizures into rigorous, quantitative approaches to invasive therapy.

Thalamic nuclei volumes and network in juvenile myoclonic epilepsy

OBJECTIVE

The aim of this study was to investigate the alterations of thalamic nuclei volumes and intrinsic thalamic networks in patients with juvenile myoclonic epilepsy (JME) compared to healthy controls.

METHODS

We enrolled 50 patients with JME and 42 healthy controls. We obtained structural volumes of the individual thalamic nuclei based on T1-weighted imaging and performed intrinsic thalamic network analysis using graph theoretical analysis. We analyzed the differences of thalamic nuclei volumes and intrinsic thalamic networks between the patients with JME and healthy controls.

RESULTS

In the patients with JME, there were significant alterations of thalamic nuclei volumes compared to healthy controls. Right laterodorsal and left suprageniculate nuclei volumes were significantly increased (0.0019% vs 0.0014%, P < .0001; 0.0011% vs 0.0008%, P = .0006, respectively), whereas left ventral posterolateral, left ventromedial, and left pulvinar inferior nuclei volumes (0.0572% vs 0.0664%, P = .0001; 0.0013% vs 0.0015%, P = .0002; 0.0120% vs 0.0140%, P < .0001, respectively) were decreased in the patients with JME. Furthermore, the intrinsic thalamic network of the patients with JME was significantly different from that of the healthy controls. The modularity in the patients with JME was significantly increased over that in healthy controls (0.0785 vs 0.0212, P = .039).

CONCLUSION

We found that there were significant alterations of thalamic nuclei volumes and intrinsic thalamic networks in patients with JME compared to healthy controls. These findings might contribute to the underlying pathogenesis of in JME.

Analysis of brain connectivity during nitrous oxide sedation using graph theory

Nitrous oxide, the least potent inhalation anesthetic, is widely used for conscious sedation. Recently, it has been reported that the occurrence of anesthetic-induced loss of consciousness decreases the interconnection between brain regions, resulting in brain network changes. However, few studies have investigated these changes in conscious sedation using nitrous oxide. Therefore, the present study aimed to use graph theory to analyze changes in brain networks during nitrous oxide sedation. Participants were 20 healthy volunteers (10 men and 10 women, 20–40 years old) with no history of systemic disease. We acquired electroencephalogram (EEG) recordings of 32 channels during baseline, nitrous oxide inhalation sedation, and recovery. EEG epochs from the baseline and the sedation state (50% nitrous oxide) were extracted and analyzed with the network connection parameters of graph theory. Analysis of 1/f dynamics, revealed a steeper slope while in the sedation state than during the baseline. Network connectivity parameters showed significant differences between the baseline and sedation state, in delta, alpha1, alpha2, and beta2 frequency bands. The most pronounced differences in functional distance during nitrous oxide sedation were observed in the alpha1 and alpha2 frequency bands. Change in 1/f dynamics indicates that changes in brain network systems occur during nitrous oxide administration. Changes in network parameters imply that nitrous oxide interferes with the efficiency of information integration in the frequency bands important for cognitive processes and attention tasks. Alteration of brain network during nitrous oxide administration may be associated to the sedative mechanism of nitrous oxide.

Effects of unilateral cortical resection of the visual cortex on bilateral human white matter

Children with unilateral resections of ventral occipito-temporal cortex (VOTC) typically do not evince visual perceptual impairments, even when relatively large swathes of VOTC are resected. In search of possible explanations for this behavioral competence, we evaluated white matter microstructure and connectivity in eight pediatric epilepsy patients following unilateral cortical resection and 15 age-matched controls. To uncover both local and broader resection-induced effects, we analyzed tractography data using two complementary approaches. First, the microstructural properties were measured in the inferior longitudinal and the inferior fronto-occipital fasciculi, the major VOTC association tracts. Group differences were only evident in the ipsilesional, and not in the contralesional, hemisphere, and single-subject analyses revealed that these differences were limited to the site of the resection. Second, graph theory was used to characterize the connectivity of the contralesional occipito-temporal regions. There were no changes to the network properties in patients with left VOTC resections nor in patients with resections outside the VOTC, but altered network efficiency was observed in two cases with right VOTC resections. These results suggest that, in many, although perhaps not all, cases of unilateral VOTC resections in childhood, the white matter profile in the preserved contralesional hemisphere along with residual neural activity might be sufficient for normal visual perception.

Machine learning applications in epilepsy

Machine learning leverages statistical and computer science principles to develop algorithms capable of improving performance through interpretation of data rather than through explicit instructions. Alongside widespread use in image recognition, language processing, and data mining, machine learning techniques have received increasing attention in medical applications, ranging from automated imaging analysis to disease forecasting. This review examines the parallel progress made in epilepsy, highlighting applications in automated seizure detection from electroencephalography (EEG), video, and kinetic data, automated imaging analysis and pre-surgical planning, prediction of medication response, and prediction of medical and surgical outcomes using a wide variety of data sources. A brief overview of commonly used machine learning approaches, as well as challenges in further application of machine learning techniques in epilepsy, is also presented. With increasing computational capabilities, availability of effective machine learning algorithms, and accumulation of larger datasets, clinicians and researchers will increasingly benefit from familiarity with these techniques and the significant progress already made in their application in epilepsy.

Normalization enhances brain network features that predict individual intelligence in children with epilepsy

BACKGROUND AND PURPOSE

Architecture of the cerebral network has been shown to associate with IQ in children with epilepsy. However, subject-level prediction on this basis, a crucial step toward harnessing network analyses for the benefit of children with epilepsy, has yet to be achieved. We compared two network normalization strategies in terms of their ability to optimize subject-level inferences on the relationship between brain network architecture and brain function.

MATERIALS AND METHODS

Patients with epilepsy and resting state fMRI were retrospectively identified. Brain network nodes were defined by anatomic parcellation, first in patient space (nodes defined for each patient) and again in template space (same nodes for all patients). Whole-brain weighted graphs were constructed according to pair-wise correlation of BOLD-signal time courses between nodes. The following metrics were then calculated: clustering coefficient, transitivity, modularity, path length, and global efficiency. Metrics computed on graphs in patient space were normalized to the same metric computed on a random network of identical size. A machine learning algorithm was used to predict patient IQ given access to only the network metrics.

RESULTS

Twenty-seven patients (8-18 years) comprised the final study group. All brain networks demonstrated expected small world properties. Accounting for intrinsic population heterogeneity had a significant effect on prediction accuracy. Specifically, transformation of all patients into a common standard space as well as normalization of metrics to those computed on a random network both substantially outperformed the use of non-normalized metrics.

CONCLUSION

Normalization contributed significantly to accurate subject-level prediction of cognitive function in children with epilepsy. These findings support the potential for quantitative network approaches to contribute clinically meaningful information in children with neurological disorders.

Juvenile myoclonic epilepsy has hyper dynamic functional connectivity in the dorsolateral frontal cortex

PURPOSE

Characterize the static and dynamic functional connectivity for subjects with juvenile myoclonic epilepsy (JME) using a quantitative data-driven analysis approach.

METHODS

Whole-brain resting-state functional MRI data were acquired on a 3 T whole-body clinical MRI scanner from 18 subjects clinically diagnosed with JME and 25 healthy control subjects. 2-min sliding-window approach was incorporated in the quantitative data-driven data analysis framework to assess both the dynamic and static functional connectivity in the resting brains. Two-sample t-tests were performed voxel-wise to detect the differences in functional connectivity metrics based on connectivity strength and density.

RESULTS

The static functional connectivity metrics based on quantitative data-driven analysis of the entire 10-min acquisition window of resting-state functional MRI data revealed significantly enhanced functional connectivity in JME patients in bilateral dorsolateral prefrontal cortex, dorsal striatum, precentral and middle temporal gyri. The dynamic functional connectivity metrics derived by incorporating a 2-min sliding window into quantitative data-driven analysis demonstrated significant hyper dynamic functional connectivity in the dorsolateral prefrontal cortex, middle temporal gyrus and dorsal striatum. Connectivity strength metrics (both static and dynamic) can detect more extensive functional connectivity abnormalities in the resting-state functional networks (RFNs) and depict also larger overlap between static and dynamic functional connectivity results.

CONCLUSION

Incorporating a 2-min sliding window into quantitative data-driven analysis of resting-state functional MRI data can reveal additional information on the temporally fluctuating RFNs of the human brain, which indicate that RFNs involving dorsolateral prefrontal cortex have temporal varying hyper dynamic characteristics in JME patients. Assessing dynamic along with static functional connectivity may provide further insights into the abnormal function connectivity underlying the pathological brain functioning in JME.

Mutual Information Better Quantifies Brain Network Architecture in Children with Epilepsy

Purpose

Metrics of the brain network architecture derived from resting-state fMRI have been shown to provide physiologically meaningful markers of IQ in children with epilepsy. However, traditional measures of functional connectivity (FC), specifically the Pearson correlation, assume a dominant linear relationship between BOLD time courses; this assumption may not be valid. Mutual information is an alternative measure of FC which has shown promise in the study of complex networks due to its ability to flexibly capture association of diverse forms. We aimed to compare network metrics derived from mutual information-defined FC to those derived from traditional correlation in terms of their capacity to predict patient-level IQ.

Materials and Methods

Patients were retrospectively identified with the following: (1) focal epilepsy; (2) resting-state fMRI; and (3) full-scale IQ by a neuropsychologist. Brain network nodes were defined by anatomic parcellation. Parcellation was performed at the size threshold of 350 mm², resulting in networks containing 780 nodes. Whole-brain, weighted graphs were then constructed according to the pairwise connectivity between nodes. In the traditional condition, edges (connections) between each pair of nodes were defined as the absolute value of the Pearson correlation coefficient between their BOLD time courses. In the mutual information condition, edges were defined as the mutual information between time courses. The following metrics were then calculated for each weighted graph: clustering coefficient, modularity, characteristic path length, and global efficiency. A machine learning algorithm was used to predict the IQ of each individual based on their network metrics. Prediction accuracy was assessed as the fractional variation explained for each condition.

Results

Twenty-four patients met the inclusion criteria (age: 8-18 years). All brain networks demonstrated expected small-world properties. Network metrics derived from mutual information-defined FC significantly outperformed the use of the Pearson correlation. Specifically, fractional variation explained was 49% (95% CI: 46%, 51%) for the mutual information method; the Pearson correlation demonstrated a variation of 17% (95% CI: 13%, 19%).

Conclusion

Mutual information-defined functional connectivity captures physiologically relevant features of the brain network better than correlation.

Clinical Relevance

Optimizing the capacity to predict cognitive phenotypes at the patient level is a necessary step toward the clinical utility of network-based biomarkers.

Link Prediction Investigation of Dynamic Information Flow in Epilepsy

As a brain disorder, epilepsy is characterized with abnormal hypersynchronous neural firings. It is known that seizures initiate and propagate in different brain regions. Long-term intracranial multichannel electroencephalography (EEG) reflects broadband ictal activity under seizure occurrence. Network-based techniques are efficient in discovering brain dynamics and offering finger-print features for specific individuals. In this study, we adopt link prediction for proposing a novel workflow aiming to quantify seizure dynamics and uncover pathological mechanisms of epilepsy. A dataset of EEG signals was enrolled that recorded from 8 patients with 3 different types of pharmocoresistant focal epilepsy. Weighted networks are obtained from phase locking value (PLV) in subband EEG oscillations. Common neighbor (CN), resource allocation (RA), Adamic-Adar (AA), and Sorenson algorithms are brought in for link prediction performance comparison. Results demonstrate that RA outperforms its rivals. Similarity, matrix was produced from the RA technique performing on EEG networks later. Nodes are gathered to form sequences by selecting the ones with the highest similarity. It is demonstrated that variations are in accordance with seizure attack in node sequences of gamma band EEG oscillations. What is more, variations in node sequences monitor the total seizure journey including its initiation and termination.

Predicting seizure outcome of vagus nerve stimulation using MEG-based network topology

Vagus nerve stimulation (VNS) is a low-risk surgical option for patients with drug resistant epilepsy, although it is impossible to predict which patients may respond to VNS treatment. Resting-state magnetoencephalography (rs-MEG) connectivity analysis has been increasingly utilized to investigate the impact of epilepsy on brain networks and identify alteration of these networks after different treatments; however, there is no study to date utilizing this modality to predict the efficacy of VNS treatment. We investigated whether the rs-MEG network topology before VNS implantation can be used to predict efficacy of VNS treatment. Twenty-three patients with epilepsy who had MEG before VNS implantation were included in this study. We also included 89 healthy control subjects from the Human Connectome Project. Using the phase-locking value in the theta, alpha, and beta frequency bands as a measure of rs-MEG functional connectivity, we calculated three global graph measures: modularity, transitivity, and characteristic path length (CPL). Our results revealed that the rs-MEG graph measures were significantly heritable and had an overall good test-retest reliability, and thus these measures may be used as potential biomarkers of the network topology. We found that the modularity and transitivity in VNS responders were significantly larger and smaller, respectively, than those observed in VNS non-responders. We also observed that the modularity and transitivity in three frequency bands and CPL in delta and beta bands were significantly different in controls than those found in responders or non-responders, although the values of the graph measures in controls were closer to those of responders than non-responders. We used the modularity and transitivity as input features of a naïve Bayes classifier, and achieved an accuracy of 87% in classification of non-responders, responders, and controls. The results of this study revealed that MEG-based graph measures are reliable biomarkers, and that these measures may be used to predict seizure outcome of VNS treatment.

Progressive topological disorganization of brain network in focal epilepsy

OBJECTIVE

Increasing evidence has suggested that epilepsy is a network disease. Graph theory is a mathematical tool that allows for the analysis and quantification of the brain network. We aimed to evaluate the influences of duration of epilepsy on the topological organization of brain network in focal epilepsy patients with normal MRI using the graph theoretical analysis based on diffusion tenor imaging.

METHODS

We prospectively enrolled 66 patients with focal epilepsy (18/66 patients were newly diagnosed) and 84 healthy subjects. All of the patients with epilepsy had normal MRI on visual inspection. All of the subjects underwent diffusion tensor imaging that was analyzed using graph theory to obtain network measures.

RESULTS

The measures of characteristic path length and small-worldness in the patients with focal epilepsy were significantly decreased, even after multiple corrections (P < .01). Moreover, the measures including mean clustering coefficient and global efficiency in the patients with epilepsy had strong tendency to decrease compared to those in healthy subjects (P = .0153 and P = .0138, respectively). When comparing the measures among the patients with newly diagnosed/chronic epilepsy and healthy subjects using ANOVA, the characteristic path length (P = .006), small-worldness (P = .032), and global efficiency (P = .004) were significantly different. In addition, the duration of epilepsy was negatively correlated with global efficiency (r = -.249, P = .0454).

CONCLUSIONS

We newly found a progressive topological disorganization of the brain network in focal epilepsy. In addition, we demonstrated disrupted topological organization in focal epilepsy, shifting toward a more random state.