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Temporal lobe epilepsy (TLE) is the most common epilepsy syndrome that empirically represents a network disorder, which makes graph theory (GT) a practical approach to understand it. Multi-shell diffusion-weighted imaging (DWI) was obtained from 89 TLE and 50 controls. GT measures extracted from harmonized DWI matrices were used as factors in a support vector machine (SVM) analysis to discriminate between groups, and in a k-means algorithm to find intrinsic structural phenotypes within TLE. SVM was able to predict group membership (mean accuracy = 0.70, area under the curve (AUC) = 0.747, Brier score (BS) = 0.264) using 10-fold cross-validation. In addition, k-means clustering identified 2 TLE clusters: 1 similar to controls, and 1 dissimilar. Clusters were significantly different in their distribution of cognitive phenotypes, with the Dissimilar cluster containing the majority of TLE with cognitive impairment (χ2 = 6.641, P = 0.036). In addition, cluster membership showed significant correlations between GT measures and clinical variables. Given that SVM classification seemed driven by the Dissimilar cluster, SVM analysis was repeated to classify Dissimilar versus Similar + Controls with a mean accuracy of 0.91 (AUC = 0.957, BS = 0.189). Altogether, the pattern of results shows that GT measures based on connectome DWI could be significant factors in the search for clinical and neurobehavioral biomarkers in TLE.

The relationship between temporal lobe epilepsy and psychopathology has had a long and contentious history with diverse views regarding the presence, nature and severity of emotional–behavioural problems in this patient population. To address these controversies, we take a new person-centred approach through the application of unsupervised machine learning techniques to identify underlying latent groups or behavioural phenotypes. Addressed are the distinct psychopathological profiles, their linked frequency, patterns and severity and the disruptions in morphological and network properties that underlie the identified latent groups. A total of 114 patients and 83 controls from the Epilepsy Connectome Project were administered the Achenbach System of Empirically Based Assessment inventory from which six Diagnostic and Statistical Manual of Mental Disorders-oriented scales were analysed by unsupervised machine learning analytics to identify latent patient groups. Identified clusters were contrasted to controls as well as to each other in order to characterize their association with sociodemographic, clinical epilepsy and morphological and functional imaging network features. The concurrent validity of the behavioural phenotypes was examined through other measures of behaviour and quality of life. Patients overall exhibited significantly higher (abnormal) scores compared with controls. However, cluster analysis identified three latent groups: (i) unaffected, with no scale elevations compared with controls (Cluster 1, 37%); (ii) mild symptomatology characterized by significant elevations across several Diagnostic and Statistical Manual of Mental Disorders-oriented scales compared with controls (Cluster 2, 42%); and (iii) severe symptomatology with significant elevations across all scales compared with controls and the other temporal lobe epilepsy behaviour phenotype groups (Cluster 3, 21%). Concurrent validity of the behavioural phenotype grouping was demonstrated through identical stepwise links to abnormalities on independent measures including the National Institutes of Health Toolbox Emotion Battery and quality of life metrics. There were significant associations between cluster membership and sociodemographic (handedness and education), cognition (processing speed), clinical epilepsy (presence and lifetime number of tonic–clonic seizures) and neuroimaging characteristics (cortical volume and thickness and global graph theory metrics of morphology and resting-state functional MRI). Increasingly dispersed volumetric abnormalities and widespread disruptions in underlying network properties were associated with the most abnormal behavioural phenotype. Psychopathology in these patients is characterized by a series of discrete latent groups that harbour accompanying sociodemographic, clinical and neuroimaging correlates. The underlying neurobiological patterns suggest that the degree of psychopathology is linked to increasingly dispersed abnormal brain networks. Similar to cognition, machine learning approaches support a novel developing taxonomy of the comorbidities of epilepsy.

Purpose The neuropsychological complications of temporal lobe epilepsy are characterized by a spectrum of reproducible cognitive phenotypes that vary in the presence, type and degree of impairment. The nature of the disruptions to the neuropsychological networks that underlie these phenotypes remain to be characterized and represent the subject of this investigation. Methods Participants included 30 healthy controls and 104 patients with temporal lobe epilepsy who fell into three cognitive phenotypes (intact, focal impairment, generalized impairment). Eighteen neuropsychological measures representing multiple cognitive domains (language, memory, executive function, visuoperception, motor speed) were examined by graph theory techniques within the control and each epilepsy cognitive phenotype group to characterize their global and local network properties. Results Across the control and epilepsy cognitive phenotype groups (intact to focal to generalized impairment), there was: 1) an orderly breakdown in the positive manifold reflected by a stepwise reduction of positive associations among the neuropsychological tests, 2) stepwise abnormal increases in global measures including the normalized clustering coefficient and modularity index, 3) stepwise abnormal decreases in normalized global efficiency, 4) a community structure demonstrating well organized modules within the control group while each epilepsy group showed deviations from controls, and 5) lower strength, compared to controls, across 8 nodes in the focal and generalized impairment groups compared to only 3 nodes in the no-impairment epilepsy group, pointing to the superior integration of local connections in controls. Discussion The cognitive phenotypes of temporal lobe epilepsy are characterized by orderly abnormalities in their underlying neuropsychological networks. These findings inform the network perturbations that underlie the taxonomy of cognitive abnormality in temporal lobe epilepsy and provide a model for examination of similar issues in other focal and generalized epilepsies.

Background: Functional Magnetic Resonance Imaging (fMRI) is a presurgical planning technique used to localize functional cortex so as to maximize resection of diseased tissue and avoid viable tissue. In this retrospective study, we examined differences in morbidity and mortality of brain tumor patients who received preoperative fMRI in comparison to those who did not. Methods: Brain tumor patients (n = 206) were selected from a retrospective review of neurosurgical case logs from 2001 to 2009 at the University of Wisconsin-Madison. Results: Univariate analysis showed improved mortality in the fMRI group and the fMRI + Electrical Cortical Stimulation Mapping (ECM) group compared to the No-fMRI group. Multivariate analyses showed improved mortality of the fMRI group and the fMRI + ECM group compared to the No-fMRI group, with age and tumor grade being the most significant influencers. Overall, the fMRI group showed survival benefits at 3 years; twice that of the No-fMRI group. Furthermore, patients with high-grade tumors showed significant survival benefits in the fMRI group, while patients with low-grade tumors did not (controlling for age and ECM). There was also a significant difference in the two groups with respect to morbidity, with patients receiving fMRI showing improved outcomes in the motor and language domains. Conclusions: This study analyzing a large retrospective series of brain tumor patients with and without the use of fMRI in the preoperative planning has resulted in improved mortality and morbidity outcomes with the use of fMRI. These results point to the importance of incorporating fMRI in presurgical planning in the clinical management of patients with brain tumors. Keywords: Brain mapping, Brain tumors, Functional neuroimaging

Purpose Psychomotor slowing is a common but understudied cognitive impairment in epilepsy. Here we test the hypothesis that psychomotor slowing is associated with alterations in brain status reflected through network analysis of large scale structural networks. We test the hypothesis that children with epilepsy with cognitive slowing at diagnosis will exhibit a cross-sectional and prospective pattern of altered brain development. Methods A total of 78 children (age 8–18) with new/recent idiopathic epilepsies underwent 1.5 T MRI with network analysis of cortical, subcortical and cerebellar volumes. Children with epilepsy were divided into slow and fast psychomotor speed groups (adjusted for age, intelligence and epilepsy syndrome). Results At baseline, slow-speed performers (SSP) presented lower modularity, lower global efficiency, higher transitivity, and lower number of hubs than fast-speed performers (FSP). Community structure in SSP exhibited poor association between cortical regions and both subcortical structures and the cerebellum while FSP presented well-defined communities. Prospectively, SSP displayed lower modularity but higher global efficiency and transitivity compared to FSP. Modules in FSP showed higher integration between and within themselves compared to SSP. SSP showed hubs mainly from frontal and temporal regions while in FSP were spread among frontal, temporal, parietal, subcortical areas and the left cerebellum. Implications Results suggest the presence of widespread alterations in large scale networks between fast- and slow-speed children with recent onset epilepsies both at baseline and 2 years later. Slower processing speed appears to be a marker of abnormal brain development antecedent to epilepsy onset as well as brain development over the 2 years following diagnosis., Highlights • Baseline: slow-speed performers (SSP) showed lower modularity and global efficiency • They also showed higher transitivity but fewer hubs than fast-speed performers (FSP) • Prospective: SSP showed lower modularity, harmonic mean and higher transitivity • Regional volume changes seem to be occurring as one in SSP, but more modular in FSP • SSP showed hubs mainly from frontal and temporal while FSP showed them widespread

The purpose of this project was to characterize brain structure and organization in persons with active and remitted childhood onset epilepsy 50 years after diagnosis compared with healthy controls. Participants from a population-based investigation of uncomplicated childhood onset epilepsy were followed up 5 decades later. Forty-one participants had a history of childhood onset epilepsy (mean age of onset = 5.2 years, current chronological age = 56.0 years) and were compared with 48 population-based controls (mean age = 55.9 years). Of the epilepsy participants, 8 had persisting active epilepsy and in 33 the epilepsy had remitted. All participants underwent 3T MRI with subsequent vertex analysis of cortical volume, thickness, surface area and gyral complexity. In addition, cortical and subcortical volumes, including regions of the frontal, parietal, temporal, and occipital lobes, and subcortical structures including amygdala, thalamus, and hippocampus, were analyzed using graph theory techniques. There were modest group differences in traditional vertex-based analyses of cortical volume, thickness, surface area and gyral index, as well as across volumes of subcortical structures, after correction for multiple comparisons. Graph theory analyses revealed suboptimal topological structural organization with enhanced network segregation and reduced global integration in the epilepsy participants compared with controls, these patterns significantly more extreme in the active epilepsy group. Furthermore, both groups with epilepsy presented a greater number of higher Z-score regions in betweenness centrality (BC) than lower Z-score regions compared with controls. Also, contrary to the group with remitted epilepsy, patients with active epilepsy presented most of their high BC Z-score regions in subcortical areas including the amygdala, thalamus, hippocampus, pallidum, and accumbens. Overall, this population-based investigation of long term outcome (5 decades) of childhood onset epilepsy reveals persisting abnormalities, especially when examined by graph theoretical measurements, and provides new insights into the very long-term outcomes of active and remitted epilepsy. Hum Brain Mapp 38:3289-3299, 2017. © 2017 Wiley Periodicals, Inc.

ObjectiveBenign epilepsy with centrotemporal spikes (BECTS) is the most common childhood idiopathic localization-related epilepsy syndrome. BECTS presents normal routine magnetic resonance imaging (MRI); however, quantitative analytic techniques have captured subtle cortical and subcortical magnetic resonance anomalies. Network science, including graph theory (GT) analyses, facilitates understanding of brain covariance patterns, potentially informing in important ways how this common self-limiting epilepsy syndrome may impact normal patterns of brain and cognitive development.MethodsGT analyses examined the developmental covariance among cortical and subcortical regions in children with new/recent onset BECTS (n=19) and typically developing healthy controls (n=22) who underwent high-resolution MRI and cognitive assessment at baseline and 2years later. Global (transitivity, global efficiency, and modularity index [Q]) and regional measures (local efficiency and hubs) were investigated to characterize network development in each group. Associations between baseline-based GT measures and cognition at both time points addressed the implications of GT analyses for cognition and prospective cognitive development. Furthermore, an individual contribution measure was investigated, reflecting how important for cognition it is for BECTS to resemble the correlation matrices of controls.ResultsGroups exhibited similar Q and overall network configuration, with BECTS presenting significantly higher transitivity and both global and local efficiency. Furthermore, both groups presented a similar number of hubs, with BECTS showing a higher number in temporal lobe regions compared to controls. The investigated measures were negatively associated with 2-year cognitive outcomes in BECTS.SignificanceChildren with BECTS present a higher-than-normal global developmental configuration compared to controls, along with divergence from normality in terms of regional configuration. Baseline GT measures demonstrate potential as a cognitive biomarker to predict cognitive outcome in BECTS 2years after diagnosis. Similarities and differences in developmental network configurations and their implications for cognition and behavior across common epilepsy syndromes are of theoretical interest and clinical relevance.

The recent revision of the classification of the epilepsies released by the ILAE Commission on Classification and Terminology (2005-2009) has been a major development in the field. Papers in this section of the special issue explore the relevance of other techniques to examine, categorize, and classify cognitive and behavioral comorbidities in epilepsy. In this review, we investigate the applicability of graph theory to understand the impact of epilepsy on cognition compared with controls and, then, the patterns of cognitive development in normally developing children which would set the stage for prospective comparisons of children with epilepsy and controls. The overall goal is to examine the potential utility of this analytic tool and approach to conceptualize the cognitive comorbidities in epilepsy. Given that the major cognitive domains representing cognitive function are interdependent, the associations between neuropsychological abilities underlying these domains can be referred to as a cognitive network. Therefore, the architecture of this cognitive network can be quantified and assessed using graph theory methods, rendering a novel approach to the characterization of cognitive status. We first provide fundamental information about graph theory procedures, followed by application of these techniques to cross-sectional analysis of neuropsychological data in children with epilepsy compared with that of controls, concluding with prospective analysis of neuropsychological development in younger and older healthy controls. This article is part of a Special Issue entitled "The new approach to classification: Rethinking cognition and behavior in epilepsy".

Objectives Brain functional topology was investigated in patients with mesial temporal lobe epilepsy (mTLE) by means of graph theory measures in two differentially defined graphs. Measures of segregation, integration, and centrality were compared between subjects with mTLE and healthy controls (HC). Methods Eleven subjects with mTLE (age 36.5±10.9years) and 15 age-matched HC (age 36.8±14.0years) participated in this study. Both anatomically and functionally defined adjacency matrices were used to investigate the measures. Binary undirected graphs were constructed to study network segregation by calculating global clustering and modularity, and network integration by calculating local and global efficiency. Node degree and participation coefficient were also computed in order to investigate network hubs and their classification into provincial or connector hubs. Measures were investigated in a range of low to medium graph density. Results The group of patients presented lower global segregation than HC while showing higher global but lower local integration. They also failed to engage regions that comprise the default-mode network (DMN) as hubs such as bilateral medial frontal regions, PCC/precuneus complex, and right inferior parietal lobule, which were present in controls. Furthermore, the cerebellum in subjects with mTLE seemed to be playing a major role in the integration of their functional networks, which was evident through the engagement of cerebellar regions as connector hubs. Conclusions Functional networks in subjects with mTLE presented both global and local abnormalities compared to healthy subjects. Specifically, there was significant separation between groups, with lower global segregation and slightly higher global integration observed in patients. This could be indicative of a network that is working as a whole instead of in segregated or specialized communities, which could translate into a less robust network and more prone to disruption in the group with epilepsy. Furthermore, functional irregularities were also observed in the group of patients in terms of the engagement of cerebellar regions as hubs while failing to engage DMN-related areas as major hubs in the network. The use of two differentially defined graphs synergistically contributed to findings.

Objective Structural and functional magnetic resonance imaging (MRI) studies have consistently documented cortical and subcortical abnormalities in patients with juvenile myoclonic epilepsy (JME). However, little is known about how these structural abnormalities emerge from the time of epilepsy onset and how network interactions between and within cortical and subcortical regions may diverge in youth with JME compared to typically developing children. Methods We examined prospective covariations of volumetric differences derived from high-resolution structural MRI during the first 2 years of epilepsy diagnosis in a group of youth with JME (n = 21) compared to healthy controls (n = 22). We indexed developmental brain changes using graph theory by computing network metrics based on the correlation of the cortical and subcortical structural covariance near the time of epilepsy and 2 years later. Results Over 2 years, normally developing children showed modular cortical development and network integration between cortical and subcortical regions. In contrast, children with JME developed a highly correlated and less modular cortical network, which was atypically dissociated from subcortical structures. Furthermore, the JME group also presented higher clustering and lower modularity indices than controls, indicating weaker modules or communities. The local efficiency in JME was higher than controls across the majority of cortical nodes. Regarding network hubs, controls presented a higher number than youth with JME that were spread across the brain with ample representation from the different modules. In contrast, children with JME showed a lower number of hubs that were mainly from one module and comprised mostly subcortical structures. Significance Youth with JME prospectively developed a network of highly correlated cortical regions dissociated from subcortical structures during the first 2 years after epilepsy onset. The cortical-subcortical network dissociation provides converging insights into the disparate literature of cortical and subcortical abnormalities found in previous studies.

Background: Advanced neuroimaging measures along with clinical variables acquired during standard imaging protocols provide a rich source of information for brain tumor patient treatment and management. Machine learning analysis has had much recent success in neuroimaging applications for normal and patient populations and has potential, specifically for brain tumor patient outcome prediction. The purpose of this work was to construct, using the current patient population distribution, a high accuracy predictor for brain tumor patient outcomes of mortality and morbidity (i.e., transient and persistent language and motor deficits). The clinical value offered is a statistical tool to help guide treatment and planning as well as an investigation of the influential factors of the disease process. Methods: Resting state fMRI, diffusion tensor imaging, and task fMRI data in combination with clinical and demographic variables were used to represent the tumor patient population (n = 62; mean age = 51.2 yrs.) in a machine learning analysis in order to predict outcomes. Results: A support vector machine classifier with a t-test filter and recursive feature elimination predicted patient mortality (18-month interval) with 80.7% accuracy, language deficits (transient) with 74.2%, motor deficits with 71.0%, language outcomes (persistent) with 80.7% and motor outcomes with 83.9%. The most influential features of the predictors were resting fMRI connectivity, and fractional anisotropy and mean diffusivity measures in the internal capsule, brain stem and superior and inferior longitudinal fasciculi. Conclusions: This study showed that advanced neuroimaging data with machine learning methods can potentially predict patient outcomes and reveal influential factors driving the predictions. Keywords: Machine-learning, fMRI, DTI, Tumor patients, Outcome prediction

Objective The purpose of this study was to investigate regional homogeneity (ReHo) in children with new-onset drug-naive Benign Epilepsy with Centrotemporal Spikes (BECTS), chronic BECTS and healthy controls (HC) using the Regional Homogeneity (ReHo) method applied to resting state fMRI data. Methods Resting state fMRI data was collected from three groups of children aged 6–13, including new onset drug naive BECTS, chronic BECTS with medication, and HC; the data analyzed by ReHo method. Mandarin school exams scores were acquired and compared across groups. Results There were three main findings. Firstly, compared with HC, abnormally increased ReHo was observed in bilateral sensorimotor regions in new onset BECTS which normalized or even reversed in the chronic BECTS group. Secondly, enhanced ReHo was found in the left frontal language region in the two BECTS groups, with even higher ReHo value in the chronic group. Lastly, decreased ReHo was found in regions of the default mode network (DMN), bilateral occipital lobes and cerebellum in both the new onset and chronic BECTS groups, lower in chronic BECTS. Behavioral analyses of school scores showed the chronic BECTS group presented significantly lower scores compared to HC ( p Significance The coherence of low frequency fluctuations is disrupted in sensorimotor, language and DMN-related regions in new-onset BECTS. Some of these effects seem to be selectively normalized in chronic BECTS, thus allowing us to explore possible chronicity and AED-induced effects on BECTS. Abnormal ReHo in left language and DMN regions could be responsible for impairments of cognitive function.

The purpose of this project was to characterize brain structure and organization in persons with active and remitted childhood onset epilepsy 50 years after diagnosis compared with healthy controls. Participants from a population-based investigation of uncomplicated childhood onset epilepsy were followed up 5 decades later. Forty-one participants had a history of childhood onset epilepsy (mean age of onset = 5.2 years, current chronological age = 56.0 years) and were compared with 48 population-based controls (mean age = 55.9 years). Of the epilepsy participants, 8 had persisting active epilepsy and in 33 the epilepsy had remitted. All participants underwent 3T MRI with subsequent vertex analysis of cortical volume, thickness, surface area and gyral complexity. In addition, cortical and subcortical volumes, including regions of the frontal, parietal, temporal, and occipital lobes, and subcortical structures including amygdala, thalamus, and hippocampus, were analyzed using graph theory techniques. There were modest group differences in traditional vertex-based analyses of cortical volume, thickness, surface area and gyral index, as well as across volumes of subcortical structures, after correction for multiple comparisons. Graph theory analyses revealed suboptimal topological structural organization with enhanced network segregation and reduced global integration in the epilepsy participants compared with controls, these patterns significantly more extreme in the active epilepsy group. Furthermore, both groups with epilepsy presented a greater number of higher Z-score regions in betweenness centrality (BC) than lower Z-score regions compared with controls. Also, contrary to the group with remitted epilepsy, patients with active epilepsy presented most of their high BC Z-score regions in subcortical areas including the amygdala, thalamus, hippocampus, pallidum, and accumbens. Overall, this population-based investigation of long term outcome (5 decades) of childhood onset epilepsy reveals persisting abnormalities, especially when examined by graph theoretical measurements, and provides new insights into the very long-term outcomes of active and remitted epilepsy. Hum Brain Mapp 38:3289-3299, 2017. © 2017 Wiley Periodicals, Inc.

Objective Normal cognitive function is defined by harmonious interaction among multiple neuropsychological domains. Epilepsy has a disruptive effect on cognition, but how diverse cognitive abilities differentially interact with one another compared with healthy controls (HC) is unclear. This study used graph theory to analyze the community structure of cognitive networks in adults with temporal lobe epilepsy (TLE) compared with that in HC. Methods Neuropsychological assessment was performed in 100 patients with TLE and 82 HC. For each group, an adjacency matrix was constructed representing pair-wise correlation coefficients between raw scores obtained in each possible test combination. For each cognitive network, each node corresponded to a cognitive test; each link corresponded to the correlation coefficient between tests. Global network structure, community structure, and node-wise graph theory properties were qualitatively assessed. Results The community structure in patients with TLE was composed of fewer, larger, more mixed modules, characterizing three main modules representing close relationships between the following: 1) aspects of executive function (EF), verbal and visual memory, 2) speed and fluency, and 3) speed, EF, perception, language, intelligence, and nonverbal memory. Conversely, controls exhibited a relative division between cognitive functions, segregating into more numerous, smaller modules consisting of the following: 1) verbal memory, 2) language, perception, and intelligence, 3) speed and fluency, and 4) visual memory and EF. Overall node-wise clustering coefficient and efficiency were increased in TLE. Significance Adults with TLE demonstrate a less clear and poorly structured segregation between multiple cognitive domains. This panorama suggests a higher degree of interdependency across multiple cognitive domains in TLE, possibly indicating compensatory mechanisms to overcome functional impairments.

Healthy aging is associated with decline of cognitive functions. However, even before those declines become noticeable, the neural architecture underlying those mechanisms has undergone considerable restructuring and reorganization. During performance of a cognitive task, not only have the task-relevant networks demonstrated reorganization with aging, which occurs primarily by recruitment of additional areas to preserve performance, but the task-irrelevant network of the "default-mode" network (DMN), which is normally deactivated during task performance, has also consistently shown reduction of this deactivation with aging. Here, we revisited those age-related changes in task-relevant (i.e., language system) and task-irrelevant (i.e., DMN) systems with a language production paradigm in terms of task-induced activation/deactivation, functional connectivity, and context-dependent correlations between the two systems. Our task fMRI data demonstrated a late increase in cortical recruitment in terms of extent of activation, only observable in our older healthy adult group, when compared to the younger healthy adult group, with recruitment of the contralateral hemisphere, but also other regions from the network previously underutilized. Our middle-aged individuals, when compared to the younger healthy adult group, presented lower levels of activation intensity and connectivity strength, with no recruitment of additional regions, possibly reflecting an initial, uncompensated, network decline. In contrast, the DMN presented a gradual decrease in deactivation intensity and deactivation extent (i.e., low in the middle-aged, and lower in the old) and similar gradual reduction of functional connectivity within the network, with no compensation. The patterns of age-related changes in the task-relevant system and DMN are incongruent with the previously suggested notion of anti-correlation of the two systems. The context-dependent correlation by psycho-physiological interaction (PPI) analysis demonstrated an independence of these two systems, with the onset of task not influencing the correlation between the two systems. Our results suggest that the language network and the DMN may be non-dependent systems, potentially correlated through the re-allocation of cortical resources, and that aging may affect those two systems differently.

Anxiety disorders represent a prevalent psychiatric comorbidity in both adults and children with epilepsy for which the etiology remains controversial. Neurobiological contributions have been suggested, but only limited evidence suggests abnormal brain volumes particularly in children with epilepsy and anxiety. Since the brain develops in an organized fashion, covariance analyses between different brain regions can be investigated as a network and analyzed using graph theory methods. We examined 46 healthy children (HC) and youth with recent onset idiopathic epilepsies with (n = 24) and without (n = 62) anxiety disorders. Graph theory (GT) analyses based on the covariance between the volumes of 85 cortical/subcortical regions were investigated. Both groups with epilepsy demonstrated less inter-modular relationships in the synchronization of cortical/subcortical volumes compared to controls, with the epilepsy and anxiety group presenting the strongest modular organization. Frontal and occipital regions in non-anxious epilepsy, and areas throughout the brain in children with epilepsy and anxiety, showed the highest centrality compared to controls. Furthermore, most of the nodes correlating to amygdala volumes were subcortical structures, with the exception of the left insula and the right frontal pole, which presented high betweenness centrality (BC); therefore, their influence in the network is not necessarily local but potentially influencing other more distant regions. In conclusion, children with recent onset epilepsy and anxiety demonstrate large scale disruptions in cortical and subcortical brain regions. Network science may not only provide insight into the possible neurobiological correlates of important comorbidities of epilepsy, but also the ways that cortical and subcortical disruption occurs., Highlights • The group with epilepsy and anxiety presented the strongest modular organization. • Frontal and occipital areas had high centrality in non-anxious epilepsy. • Anxious epilepsy had high centrality throughout the brain compared to controls. • The left insula and the right frontal pole were hubs in anxious epilepsy. • Most nodes correlating to amygdala in anxious epilepsy were subcortical structures.

Summary Objective Benign epilepsy with centrotemporal spikes (BECTS), the most common focal childhood epilepsy, is associated with subtle abnormalities in cognition and possible developmental alterations in brain structure when compared to healthy participants, as indicated by previous cross-sectional studies. To examine the natural history of BECTS, we investigated cognition, cortical thickness, and subcortical volumes in children with new/recent onset BECTS and healthy controls (HC). Methods Participants were 8–15 years of age, including 24 children with new-onset BECTS and 41 age- and gender-matched HC. At baseline and 2 years later, all participants completed a cognitive assessment, and a subset (13 BECTS, 24 HC) underwent T1 volumetric magnetic resonance imaging (MRI) scans focusing on cortical thickness and subcortical volumes. Results Baseline cognitive abnormalities associated with BECTS (object naming, verbal learning, arithmetic computation, and psychomotor speed/dexterity) persisted over 2 years, with the rate of cognitive development paralleling that of HC. Baseline neuroimaging revealed thinner cortex in BECTS compared to controls in frontal, temporal, and occipital regions. Longitudinally, HC showed widespread cortical thinning in both hemispheres, whereas BECTS participants showed sparse regions of both cortical thinning and thickening. Analyses of subcortical volumes showed larger left and right putamens persisting over 2 years in BECTS compared to HC. Significance Cognitive and structural brain abnormalities associated with BECTS are present at onset and persist (cognition) and/or evolve (brain structure) over time. Atypical maturation of cortical thickness antecedent to BECTS onset results in early identified abnormalities that continue to develop abnormally over time. However, compared to anatomic development, cognition appears more resistant to further change over time.

Copyright © 2015 Garcia-Ramos et al.This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Traditional approaches to understanding cognition in children with epilepsy (CWE) involve cross-sectional or prospective examination of diverse test measures, an approach that does not inform the interrelationship between different abilities or how interrelationships evolve prospectively. Here we utilize graph theory techniques to interrogate the development of cognitive landmarks in CWE and healthy controls (HC) using the two-year percentage change across 20 tests. Additionally, we characterize the development of cognition using traditional analyses, showing that CWE perform worse at baseline, develop in parallel with HC, statically maintaining cognitive differences two years later. Graph analyses, however, showed CWE to exhibit both lower integration and segregation in development of their cognitive networks compared to HC. In conclusion, graph analyses of neuropsychological data capture a dynamic and changing complexity in the interrelationships among diverse cognitive skills, maturation of the cognitive network over time, and the nature of differences between normally developing children and CWE.

Stroke remains a major source of disability in the United States as well as in many industrialized countries. Although most patients survive acute stroke and some spontaneous recovery is common, the restoration of function is rarely complete, leaving persistent deficits that affect a patient's activities of daily living. While the structural damage is usually rather localized, the functional damage can be vast, impacting regions physically remote from the area of infarct. Functional magnetic resonance imaging (fMRI) allows us to pursue, noninvasively, the investigation of stroke-related disruptions and brain recovery from such injuries. There is an emerging consensus from these fMRI studies in terms of the recovery process of the brain after a stroke. Task-based motor fMRI studies with patients exhibiting motor deficit have revealed initial recruitment of perilesional areas to the infarcted regions of the primary motor cortex as well as the homologous area in the contralesional hemisphere reaching high levels of activity at the subacute stages of recovery. The recruitment of such activity is thought to provide assistance initially in the execution of the motor functional task. This activity is then reduced in the later stages of recovery, mostly associated with further improvement of motor functions. Similarly, in patients exhibiting language deficit, perilesional areas to the infarcted regions of the language areas as well as the homologous areas in the contralesional hemisphere are recruited, reaching high levels of activity at the subacute stages of recovery and overall improvement of language function. This activity is then reduced in the later stages of recovery, mostly associated with further improvement of language functions. Although there is no clear consensus, most of these studies suggest a benefit of suppression of contralesional activation over time, which has shown promise in therapeutic rehabilitation strategies such as constraint-induced therapy and noninvasive brain stimulation methods. A third type of deficit that is common in stroke patients is hemispatial neglect caused by lesions that affect the directing of attention (the dorsal attention network). There is some fMRI evidence suggesting that recovery mechanisms within such a population involves reestablishment of the weighted activity within the dorsal attention network as well as involvement of a ventral attention network. Moreover, from analysis of networks in patients with stroke injury, interhemispheric connectivity initially disrupted by the onset of the injury is preferentially reestablished in the later stages if possible. Regaining of interhemispheric connectivity does not necessarily translate into increased activity, but rather translates into better communication between the two hemispheres, excitatory or inhibitory. There also exists a reestablishment of greater top-down modulatory control of higher level cortical areas on the lower cortices, such as premotor cortex exerting modulatory influence on the primary motor cortex in the motor system. Although the brain organization of a recovered patient returns to a “normal-like” pattern of activation with better recovery, graph theoretical analyses have demonstrated that neural reorganization after stroke may progress through the establishment of nonoptimal network structures, with the incorporation of previously unrecruited areas. Altogether, fMRI has consistently been proved to be a valuable and pragmatic tool in the investigation of the underlying mechanisms of stroke impairment and recovery. This allows us to hope that better treatment strategies will soon emanate and decrease the persistent burden that the injury of stroke imposes on the patient and society. Additionally, the use of fMRI provides several prospects in rehabilitation strategies such as future use of fMRI in real-time feedback modulation of neural activity in therapeutic protocols.