Presurgery resting-state local graph-theory measures predict neurocognitive outcomes after brain surgery in temporal lobe epilepsy

Clinical utility of Epitrack for differentiating profiles and patterns of post-surgical change in memory and quality of life in patients with drug-resistant epilepsy

PURPOSE

To assess whether performance in attention and executive functions evaluated with the Epitrack screening tool before surgery can differentiate memory and quality of life (QOL) profiles, and detect different post-surgical change patterns in these variables in patients with epilepsy.

METHODS

This is a longitudinal study. Seventy-seven patients with drug-resistant epilepsy (mean age = 37.91) underwent a neuropsychological assessment before and one year after surgery. Epitrack, a screening tool that exclusively evaluates attention and executive functioning, was administered in the pre-surgical assessment, and verbal and visual memory and QOL were assessed before and after surgery.

RESULTS

Patients with impaired Epitrack performance had poorer verbal and visual memory than those with intact Epitrack performance, regardless of the time point (for all, p < 0.0001). They also showed a post-surgical decline in immediate verbal recall (p = 0.04) and discriminability (p = 0.001). Patients with intact Epitrack performance did not exhibit this decline. Epitrack total score significantly contributed to 13 and 11% of the variance of post-surgical changes in immediate verbal recall and discriminability, respectively. Epitrack groups did not differ in QOL profiles or changes, but post-surgical immediate verbal recall improvements were related to post-surgical QOL improvements.

CONCLUSION

Our findings underline the utility of Epitrack screening tool to detect different patterns of verbal and visual memory dysfunction, as well as to predict post-surgical verbal memory decline in patients with drug-resistant epilepsy. Patients with lower pre-surgical Epitrack scores appear to be at increased risk for post-surgical memory decline.

Leveraging the resting brain to predict memory decline after temporal lobectomy

OBJECTIVE

Predicting memory morbidity after temporal lobectomy in patients with temporal lobe epilepsy (TLE) relies on indices of preoperative temporal lobe structural and functional integrity. However, epilepsy is increasingly considered a network disorder, and memory a network phenomenon. We assessed the utility of functional network measures to predict postoperative memory changes.

METHODS

Seventy-two adults with TLE (37 left/35 right) underwent preoperative resting-state functional magnetic resonance imaging and pre- and postoperative neuropsychological assessment. We compared functional connectivity throughout the memory network of each patient to a healthy control template (n = 19) to identify differences in global organization. A second metric indicated the degree of integration of the to-be-resected temporal lobe with the rest of the memory network. We included these measures in a linear regression model alongside standard clinical variables as predictors of memory change after surgery.

RESULTS

Left TLE patients with more atypical memory networks, and with greater functional integration of the to-be-resected region with the rest of the memory network preoperatively, experienced the greatest decline in verbal memory after surgery. Together, these two measures explained 44% of variance in verbal memory change, outperforming standard clinical and demographic variables. None of the variables examined was associated with visuospatial memory change in patients with right TLE.

SIGNIFICANCE

Resting-state connectivity provides valuable information concerning both the integrity of to-be-resected tissue and functional reserve across memory-relevant regions outside of the to-be-resected tissue. Intrinsic functional connectivity has the potential to be useful for clinical decision-making regarding memory outcomes in left TLE, and more work is needed to identify the factors responsible for differences seen in right TLE.

Edge and modular significance assessment in individual-specific networks

Individual-specific networks, defined as networks of nodes and connecting edges that are specific to an individual, are promising tools for precision medicine. When such networks are biological, interpretation of functional modules at an individual level becomes possible. An under-investigated problem is relevance or "significance" assessment of each individual-specific network. This paper proposes novel edge and module significance assessment procedures for weighted and unweighted individual-specific networks. Specifically, we propose a modular Cook's distance using a method that involves iterative modeling of one edge versus all the others within a module. Two procedures assessing changes between using all individuals and using all individuals but leaving one individual out (LOO) are proposed as well (LOO-ISN, MultiLOO-ISN), relying on empirically derived edges. We compare our proposals to competitors, including adaptions of OPTICS, kNN, and Spoutlier methods, by an extensive simulation study, templated on real-life scenarios for gene co-expression and microbial interaction networks. Results show the advantages of performing modular versus edge-wise significance assessments for individual-specific networks. Furthermore, modular Cook's distance is among the top performers across all considered simulation settings. Finally, the identification of outlying individuals regarding their individual-specific networks, is meaningful for precision medicine purposes, as confirmed by network analysis of microbiome abundance profiles.

Bilingualism and Structural Network Organization in Temporal Lobe Epilepsy: Resilience in Neurologic Disease

BACKGROUND AND OBJECTIVES

There is growing evidence that bilingualism can induce neuroplasticity and modulate neural efficiency, resulting in greater resistance to neurologic disease. However, whether bilingualism is beneficial to neural health in the presence of epilepsy is unknown. We tested whether bilingual individuals with temporal lobe epilepsy (TLE) have improved whole-brain structural white matter network organization.

METHODS

Healthy controls and individuals with TLE recruited from 2 specialized epilepsy centers completed diffusion-weighted MRI and neuropsychological testing as part of an observational cohort study. Whole-brain connectomes were generated via diffusion tractography and analyzed using graph theory. Global analyses compared network integration (path length) and specialization (transitivity) in TLE vs controls and in a 2 (left vs right TLE) × 2 (bilingual vs monolingual) model. Local analyses compared mean local efficiency of predefined frontal-executive and language (i.e., perisylvian) subnetworks. Exploratory correlations examined associations between network organization and neuropsychological performance.

RESULTS

A total of 29 bilingual and 88 monolingual individuals with TLE matched on several demographic and clinical variables and 81 age-matched healthy controls were included. Globally, a significant interaction between language status and side of seizure onset revealed higher network organization in bilinguals compared with monolinguals but only in left TLE (LTLE). Locally, bilinguals with LTLE showed higher efficiency in frontal-executive but not in perisylvian networks compared with LTLE monolinguals. Improved whole-brain network organization was associated with better executive function performance in bilingual but not monolingual LTLE.

DISCUSSION

Higher white matter network organization in bilingual individuals with LTLE suggests a neuromodulatory effect of bilingualism on whole-brain connectivity in epilepsy, providing evidence for neural reserve. This may reflect attenuation of or compensation for epilepsy-related dysfunction of the left hemisphere, potentially driven by increased efficiency of frontal-executive networks that mediate dual-language control. This highlights a potential role of bilingualism as a protective factor in epilepsy, motivating further research across neurologic disorders to define mechanisms and develop interventions.

Shared functional network abnormality in patients with temporal lobe epilepsy and their siblings

AIM

Temporal lobe epilepsy is a neurological network disease in which genetics played a greater role than previously appreciated. This study aimed to explore shared functional network abnormalities in patients with sporadic temporal lobe epilepsy and their unaffected siblings.

METHODS

Fifty-eight patients with sporadic temporal lobe epilepsy, 13 unaffected siblings, and 30 healthy controls participated in this cross-sectional study. We examined the task-based whole-brain functional network topology and the effective functional connectivity between networks identified by group-independent component analysis.

RESULTS

We observed increased global efficiency, decreased clustering coefficiency, and decreased small-worldness in patients and siblings (p < 0.05, false discovery rate-corrected). The effective network connectivity from the ventral attention network to the limbic system was impaired (p < 0.001, false discovery rate-corrected). These features had higher prevalence in unaffected siblings than in normal population and was not correlated with disease burden. In addition, topological abnormalities had a high intraclass correlation between patients and their siblings.

CONCLUSION

Patients with temporal lobe epilepsy and their unaffected siblings showed shared topological functional disturbance and the effective functional network connectivity impairment. These abnormalities may contribute to the pathogenesis that promotes the susceptibility of seizures and language decline in temporal lobe epilepsy.

White matter network organization predicts memory decline after epilepsy surgery

The authors have withdrawn their manuscript owing to a substantial change in data analysis and findings/conclusions. Therefore, the authors do not wish this work to be cited as reference for the project. If you have any questions, please contact the corresponding author.

Multimodal Triplet Attention Network for Brain Disease Diagnosis

Multi-modal imaging data fusion has attracted much attention in medical data analysis because it can provide complementary information for more accurate analysis. Integrating functional and structural multi-modal imaging data has been increasingly used in the diagnosis of brain diseases, such as epilepsy. Most of the existing methods focus on the feature space fusion of different modalities but ignore the valuable high-order relationships among samples and the discriminative fused features for classification. In this paper, we propose a novel framework by fusing data from two modalities of functional MRI (fMRI) and diffusion tensor imaging (DTI) for epilepsy diagnosis, which effectively captures the complementary information and discriminative features from different modalities by high-order feature extraction with the attention mechanism. Specifically, we propose a triple network to explore the discriminative information from the high-order representation feature space learned from multi-modal data. Meanwhile, self-attention is introduced to adaptively estimate the degree of importance between brain regions, and the cross-attention mechanism is utilized to extract complementary information from fMRI and DTI. Finally, we use the triple loss function to adjust the distance between samples in the common representation space. We evaluate the proposed method on the epilepsy dataset collected from Jinling Hospital, and the experiment results demonstrate that our method is significantly superior to several state-of-the-art diagnosis approaches.

Episodic memory network connectivity in temporal lobe epilepsy

OBJECTIVE

Temporal lobe epilepsy (TLE) affects brain networks and is associated with impairment of episodic memory. Temporal and extratemporal reorganization of memory functions is described in functional magnetic resonance imaging (fMRI) studies. Functional reorganizations have been shown at the local activation level, but network-level alterations have been underinvestigated. We aim to investigate the functional anatomy of memory networks using memory fMRI and determine how this relates to memory function in TLE.

METHODS

Ninety patients with unilateral TLE (43 left) and 29 controls performed a memory-encoding fMRI paradigm of faces and words with subsequent out-of-scanner recognition test. Subsequent memory event-related contrasts of words and faces remembered were generated. Psychophysiological interaction analysis investigated task-associated changes in functional connectivity seeding from the mesial temporal lobes (MTLs). Correlations between changes in functional connectivity and clinical memory scores, epilepsy duration, age at epilepsy onset, and seizure frequency were investigated, and between connectivity supportive of better memory and disease burden. Connectivity differences between controls and TLE, and between TLE with and without hippocampal sclerosis, were explored using these confounds as regressors of no interest.

RESULTS

Compared to controls, TLE patients showed widespread decreased connectivity between bilateral MTLs and frontal lobes, and increased local connectivity between the anterior MTLs bilaterally. Increased intrinsic connectivity within the bilateral MTLs correlated with better out-of-scanner memory performance in both left and right TLE. Longer epilepsy duration and higher seizure frequency were associated with decreased connectivity between bilateral MTLs and left/right orbitofrontal cortex (OFC) and insula, connections supportive of memory functions. TLE due to hippocampal sclerosis was associated with greater connectivity disruption within the MTL and extratemporally.

SIGNIFICANCE

Connectivity analyses showed that TLE is associated with temporal and extratemporal memory network reorganization. Increased bilateral functional connectivity within the MTL and connectivity to OFC and insula are efficient, and are disrupted by greater disease burden.

Functional Connectivity Alterations Based on Hypometabolic Region May Predict Clinical Prognosis of Temporal Lobe Epilepsy: A Simultaneous 18F-FDG PET/fMRI Study

(1) Background: Accurate localization of the epileptogenic zone and understanding the related functional connectivity (FC) alterations are critical for the prediction of clinical prognosis in patients with temporal lobe epilepsy (TLE). We aim to localize the hypometabolic region in TLE patients, compare the differences in FC alterations based on hypometabolic region and structural lesion, respectively, and explore their relationships with clinical prognosis. (2) Methods: Thirty-two TLE patients and 26 controls were recruited. Patients underwent 18F-FDG PET/MR scan, surgical treatment, and a 2−3-year follow-up. Visual assessment and voxel-wise analyses were performed to identify hypometabolic regions. ROI-based FC analyses were performed. Relationships between clinical prognosis and FC values were performed by using Pearson correlation analyses and receiver operating characteristic (ROC) analysis. (3) Results: Hypometabolic regions in TLE patients were found in the ipsilateral hippocampus, parahippocampal gyrus, and temporal lobe (p < 0.001). Functional alterations based on hypometabolic regions showed a more extensive whole-brain FC reduction. FC values of these regions negatively correlated with epilepsy duration (p < 0.05), and the ROC curve of them showed significant accuracy in predicting postsurgical outcome. (4) Conclusions: In TLE patients, FC related with hypometabolic region obtained by PET/fMRI may provide value in the prediction of disease progression and seizure-free outcome.

Verbal Learning and Longitudinal Hippocampal Network Connectivity in Temporal Lobe Epilepsy Surgery

Introduction

Learning new verbal information can be impaired in 20-40% of patients after mesial temporal lobe resection. In recent years, understanding epilepsy as a brain network disease, and investigating the relationship between large-scale resting networks and cognition has led to several advances. Aligned studies suggest that it is the integrity of the hippocampal connectivity with these large-scale networks what is relevant for cognition, with evidence showing a functional and structural heterogeneity along the long axis hippocampus bilaterally.

Objective

Our aim is to examine whether pre-operative resting-state connectivity along the long hippocampal axis is associated with verbal learning decline after anterior temporal lobe resection.

Methods

Thirty-one patients with epilepsy who underwent an anterior temporal lobe resection were pre-surgically scanned at 3-tesla, and pre/post-surgery evaluated for learning deficits using the Rey Auditory Verbal Learning Task (RAVLT). Eighteen controls matched by age, gender and handedness were also scanned and evaluated with the RAVLT. We studied the functional connectivity along the (anterior/posterior) long axis hippocampal subregions and resting-state functionally-defined brain networks involved in learning [executive (EXE), dorsal attention (DAN) and default-mode (DMN) networks]. Functional connectivity differences between the two groups of patients (learning intact or with learning decline) and controls were investigated with MANOVA and discriminant analysis.

Results

There were significant differences in the pattern of hippocampal connectivity among the groups. Regarding the anterior connectivity hippocampal pattern, our data showed an increase of connectivity in the pathological side with the DAN (p = 0.011) and the EXE (p = 0.008) when comparing learning-decline vs. learning-intact patients. Moreover, the non-pathological side showed an increase in the anterior connectivity pattern with the DAN (p = 0.027) between learning-decline vs. learning-intact patients. In contrast, the posterior hippocampus showed a reduction of connectivity in the learning-decline patients with the DMN, both in the pathological (p = 0.004) and the non-pathological sides (p = 0.036). Finally, the discriminant analysis based on the pre-operative connectivity pattern significantly differentiated the learning-decline patients from the other groups (p = 0.019).

Conclusion

Our findings reveal bilateral connectivity disruptions along the longitudinal axis of the hippocampi with resting-state networks, which could be key to identify those patients at risk of verbal learning decline after epilepsy surgery.

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.

Common functional connectivity alterations in focal epilepsies identified by machine learning

OBJECTIVE

This study was undertaken to identify shared functional network characteristics among focal epilepsies of different etiologies, to distinguish epilepsy patients from controls, and to lateralize seizure focus using functional connectivity (FC) measures derived from resting state functional magnetic resonance imaging (MRI).

METHODS

Data were taken from 103 adult and 65 pediatric focal epilepsy patients (with or without lesion on MRI) and 109 controls across four epilepsy centers. We used three whole-brain FC measures: parcelwise connectivity matrix, mean FC, and degree of FC. We trained support vector machine models with fivefold cross-validation (1) to distinguish patients from controls and (2) to lateralize the hemisphere of seizure onset in patients. We reported the regions and connections with the highest importance from each model as the common FC differences between the compared groups.

RESULTS

FC measures related to the default mode and limbic networks had higher importance relative to other networks for distinguishing epilepsy patients from controls. In lateralization models, regions related to somatosensory, visual, default mode, and basal ganglia showed higher importance. The epilepsy versus control classification model trained using a 400-parcel connectivity matrix achieved a median testing accuracy of 75.6% (median area under the curve [AUC] = .83) in repeated independent testing. Lateralization accuracy using the 400-parcel connectivity matrix reached a median accuracy of 64.0% (median AUC = .69).

SIGNIFICANCE

Machine learning models revealed common FC alterations in a heterogeneous group of patients with focal epilepsies. The distribution of the most altered regions supports the hypothesis that shared functional alteration exists beyond the seizure onset zone and its epileptic network. We showed that FC measures can distinguish patients from controls, and further lateralize focal epilepsies. Future studies are needed to confirm these findings by using larger numbers of epilepsy patients.

Prediction of the clinical and naming status after anterior temporal lobe resection in patients with epilepsy

By assessing the cognitive capital, neuropsychological evaluation (NPE) plays a vital role in the perioperative workup of patients with refractory focal epilepsy. In this retrospective study, we used cutting-edge statistical approaches to examine a group of 47 patients with refractory temporal lobe epilepsy (TLE), who underwent standard anterior temporal lobectomy (ATL). Our objective was to determine whether NPE may represent a robust predictor of the postoperative status, two years after surgery. Specifically, based on pre- and postsurgical neuropsychological data, we estimated the sensitivity of cognitive indicators to predict and to disentangle phenotypes associated with more or less favorable outcomes. Engel (ENG) scores were used to assess clinical outcome, and picture naming (NAM) performance to estimate naming status. Two methods were applied: (a) machine learning (ML) to explore cognitive sensitivity to postoperative outcomes; and (b) graph theory (GT) to assess network properties reflecting favorable vs. less favorable phenotypes after surgery. Specific neuropsychological indices assessing language, memory, and executive functions can globally predict outcomes. Interestingly, preoperative cognitive networks associated with poor postsurgical outcome already exhibit an atypical, highly modular and less densely interconnected configuration. We provide statistical and clinical tools to anticipate the condition after surgery and achieve a more personalized clinical management. Our results also shed light on possible mechanisms put in place for cognitive adaptation after acute injury of central nervous system in relation with surgery.

The Impact of Right Temporal Lobe Epilepsy On Nonverbal Memory: Meta-regression of Stimulus- and Task-related Moderators

Nonverbal memory tests have great potential value for detecting the impact of lateralized pathology and predicting the risk of memory loss following right temporal lobe resection (TLR) for temporal lobe epilepsy (TLE) patients, but this potential has not been realized. Previous reviews suggest that stimulus type moderates the capacity of nonverbal memory tests to detect right-lateralized pathology (i.e., faces > designs), but the roles of other task-related factors have not been systematically explored. We address these limitations using mixed model meta-regression (k = 158) of right-lateralization effects (right worse than left TLE) testing the moderating effects of: 1) stimulus type (designs, faces, spatial), 2) learning format (single trial, repeated trials), 3) testing delay (immediate or long delay), and 4) testing format (recall, recognition) for three patient scenarios: 1) presurgical, 2) postsurgical, and 3) postsurgical change. Stimulus type significantly moderated the size of the right-lateralization effect (faces > designs) for postsurgical patients, test format moderated the size of the right-lateralization effect for presurgical-postsurgical change (recognition > recall) but learning format and test delay had no right-lateralization effect for either sample. For presurgical patients, none of the task-related factors significantly increased right-lateralization effects. This comprehensive review reveals the value of recognition testing in gauging the risk of nonverbal memory decline.

What Can Glioma Patients Teach Us about Language (Re)Organization in the Bilingual Brain: Evidence from fMRI and MEG

Recent evidence suggests that the presence of brain tumors (e.g., low-grade gliomas) triggers language reorganization. Neuroplasticity mechanisms called into play can transfer linguistic functions from damaged to healthy areas unaffected by the tumor. This phenomenon has been reported in monolingual patients, but much less is known about the neuroplasticity of language in the bilingual brain. A central question is whether processing a first or second language involves the same or different cortical territories and whether damage results in diverse recovery patterns depending on the language involved. This question becomes critical for preserving language areas in bilingual brain-tumor patients to prevent involuntary pathological symptoms following resection. While most studies have focused on intraoperative mapping, here, we go further, reporting clinical cases for five bilingual patients tested before and after tumor resection, using a novel multimethod approach merging neuroimaging information from fMRI and MEG to map the longitudinal reshaping of the language system. Here, we present four main findings. First, all patients preserved linguistic function in both languages after surgery, suggesting that the surgical intervention with intraoperative language mapping was successful in preserving cortical and subcortical structures necessary for brain plasticity at the functional level. Second, we found reorganization of the language network after tumor resection in both languages, mainly reflected by a shift of activity to right hemisphere nodes and the recruitment of ipsilesional left nodes. Third, we found that this reorganization varied according to the language involved, indicating that L1 and L2 follow different reshaping patterns after surgery. Fourth, oscillatory longitudinal effects were correlated with BOLD laterality changes in superior parietal and middle frontal areas. These findings may reflect that neuroplasticity impacts on the compensatory involvement of executive control regions, supporting the allocation of cognitive resources as a consequence of increased attentional demands. Furthermore, these results hint at the complementary role of this neuroimaging approach in language mapping, with fMRI offering excellent spatial localization and MEG providing optimal spectrotemporal resolution.

Source-level EEG and graph theory reveal widespread functional network alterations in focal epilepsy

OBJECTIVE

The hypersynchronous neuronal activity associated with epilepsy causes widespread functional network disruptions extending beyond the epileptogenic zone. This altered network topology is considered a mediator for non-seizure symptoms, such as cognitive impairment. The aim of this study was to investigate functional network alterations in focal epilepsy patients with good seizure control and high quality of life.

METHODS

We compared twenty-two focal epilepsy patients and sixteen healthy controls on graph metrics derived from functional connectivity of source-level resting-state EEG. Graph metrics were calculated over a range of network densities in five frequency bands.

RESULTS

We observed a significantly increased small world index in patients relative to controls. On the local level, two left-hemisphere regions displayed a shift towards greater alpha band "hubness". The findings were not mediated by age, sex or education, nor by age of epilepsy onset, duration or focus lateralisation.

CONCLUSIONS

Widespread functional network alterations are evident in focal epilepsy, even in a cohort characterised by successful anti-seizure medication therapy and high quality of life. These findings might support the position that functional network analysis could hold clinical relevance for epilepsy.

SIGNIFICANCE

Focal epilepsy is accompanied by global and local functional network aberrancies which might be implied in the sustenance of non-seizure symptoms.

Recent Advances in Neuroimaging of Epilepsy

Human neuroimaging has had a major impact on the biological understanding of epilepsy and the relationship between pathophysiology, seizure management, and outcomes. This review highlights notable recent advancements in hardware, sequences, methods, analyses, and applications of human neuroimaging techniques utilized to assess epilepsy. These structural, functional, and metabolic assessments include magnetic resonance imaging (MRI), positron emission tomography (PET), and magnetoencephalography (MEG). Advancements that highlight non-invasive neuroimaging techniques used to study the whole brain are emphasized due to the advantages these provide in clinical and research applications. Thus, topics range across presurgical evaluations, understanding of epilepsy as a network disorder, and the interactions between epilepsy and comorbidities. New techniques and approaches are discussed which are expected to emerge into the mainstream within the next decade and impact our understanding of epilepsies. Further, an increasing breadth of investigations includes the interplay between epilepsy, mental health comorbidities, and aberrant brain networks. In the final section of this review, we focus on neuroimaging studies that assess bidirectional relationships between mental health comorbidities and epilepsy as a model for better understanding of the commonalities between both conditions.

Resting-state hippocampal networks related to language processing reveal unique patterns in temporal lobe epilepsy

OBJECTIVE

Patients with temporal lobe epilepsy (TLE) commonly experience a broad range of language impairments. These deficits are thought to arise from repeated seizure activity that damages language regions. However, connectivity between the seizure onset region in the hippocampus and regions related to language processing has rarely been studied, and could also have a strong impact on language function. The purpose of this study was to use resting-state functional connectivity (FC) measures to assess hippocampal network patterns and their relation to language abilities in patients with right TLE (RLTE), left TLE (LTLE), and healthy controls.

METHODS

Presurgical resting-state 3T functional MRI data were acquired from 40 patients with mesial TLE (27 RTLE, 13 LTLE) and 54 controls. The regions of interest were the anterior and posterior bilateral hippocampi and eleven regions grouped by frontal or temporo-parietal locations, including large areas of language-related cortex. FC values were computed with the right/left anterior and posterior hippocampi as the seeds and frontal and temporo-parietal regions as targets. Resting-state lateralization indices were also calculated (LI-Rest), and all FC measures were correlated to neuropsychological language scores and measures related to manifestation of epilepsy including age of onset, duration of disease, monthly seizure frequency, and hippocampal volume.

RESULTS

We found significant group differences between the anterior hippocampi and temporo-parietal regions closest to the seizure focus, in which RTLE and LTLE showed stronger connectivity to their contralateral hippocampus, while controls showed similar connectivity to both hippocampi. In addition, LI-Rest demonstrated significantly more right lateralization in LTLE compared to RTLE for temporo-parietal regions only. In LTLE, we found significant associations between stronger hippocampal network resting-state FC and later age of onset and decreased left anterior hippocampal volume.

SIGNIFICANCE

The results of our study indicate that the presence of TLE impacts hippocampal-temporo-parietal networks relevant to language processing.

Altered communication dynamics reflect cognitive deficits in temporal lobe epilepsy

OBJECTIVE

Although temporal lobe epilepsy (TLE) is recognized as a system-level disorder, little work has investigated pathoconnectomics from a dynamic perspective. By leveraging computational simulations that quantify patterns of information flow across the connectome, we tested the hypothesis that network communication is abnormal in this condition, studied the interplay between hippocampal- and network-level disease effects, and assessed associations with cognition.

METHODS

We simulated signal spreading via a linear threshold model that temporally evolves on a structural graph derived from diffusion-weighted magnetic resonance imaging (MRI), comparing a homogeneous group of 31 patients with histologically proven hippocampal sclerosis to 31 age- and sex-matched healthy controls. We evaluated the modulatory effects of structural alterations of the neocortex and hippocampus on network dynamics. Furthermore, multivariate statistics addressed the relationship with cognitive parameters.

RESULTS

We observed a slowing of in- and out-spreading times across multiple areas bilaterally, indexing delayed information flow, with the strongest effects in ipsilateral frontotemporal regions, thalamus, and hippocampus. Effects were markedly reduced when controlling for hippocampal volume but not cortical thickness, underscoring the central role of the hippocampus in whole-brain disease expression. Multivariate analysis associated slower spreading time in frontoparietal, limbic, default mode, and subcortical networks with impairment across tasks tapping into sensorimotor, executive, memory, and verbal abilities.

SIGNIFICANCE

Moving beyond descriptions of static topology toward the formulation of brain dynamics, our work provides novel insight into structurally mediated network dysfunction and demonstrates that altered whole-brain communication dynamics contribute to common cognitive difficulties in TLE.

Connectome biomarkers of drug-resistant epilepsy

Drug-resistant epilepsy (DRE) considerably affects patient health, cognition, and well-being, and disproportionally contributes to the overall burden of epilepsy. The most common DRE syndromes are temporal lobe epilepsy related to mesiotemporal sclerosis and extratemporal epilepsy related to cortical malformations. Both syndromes have been traditionally considered as "focal," and most patients benefit from brain surgery for long-term seizure control. However, increasing evidence indicates that many DRE patients also present with widespread structural and functional network disruptions. These anomalies have been suggested to relate to cognitive impairment and prognosis, highlighting their importance for patient management. The advent of multimodal neuroimaging and formal methods to quantify complex systems has offered unprecedented ability to profile structural and functional brain networks in DRE patients. Here, we performed a systematic review on existing DRE network biomarker candidates and their contribution to three key application areas: (1) modeling of cognitive impairments, (2) localization of the surgical target, and (3) prediction of clinical and cognitive outcomes after surgery. Although network biomarkers hold promise for a range of clinical applications, translation of neuroimaging biomarkers to the patient's bedside has been challenged by a lack of clinical and prospective studies. We therefore close by highlighting conceptual and methodological strategies to improve the evaluation and accessibility of network biomarkers, and ultimately guide clinically actionable decisions.

Network connectivity predicts language processing in healthy adults

Brain imaging has been used to predict language skills during development and neuropathology but its accuracy in predicting language performance in healthy adults has been poorly investigated. To address this shortcoming, we studied the ability to predict reading accuracy and single‐word comprehension scores from rest‐ and task‐based functional magnetic resonance imaging (fMRI) datasets of 424 healthy adults. Using connectome‐based predictive modeling, we identified functional brain networks with >400 edges that predicted language scores and were reproducible in independent data sets. To simplify these complex models we identified the overlapping edges derived from the three task‐fMRI sessions (language, working memory, and motor tasks), and found 12 edges for reading recognition and 11 edges for vocabulary comprehension that accounted for 20% of the variance of these scores, both in the training sample and in the independent sample. The overlapping edges predominantly emanated from language areas within the frontoparietal and default‐mode networks, with a strong precuneus prominence. These findings identify a small subset of edges that accounted for a significant fraction of the variance in language performance that might serve as neuromarkers for neuromodulation interventions to improve language performance or for presurgical planning to minimize language impairments.

Language lateralization from task-based and resting state functional MRI in patients with epilepsy

We compared resting state (RS) functional connectivity and task‐based fMRI to lateralize language dominance in 30 epilepsy patients (mean age = 33; SD = 11; 12 female), a measure used for presurgical planning. Language laterality index (LI) was calculated from task fMRI in frontal, temporal, and frontal + temporal regional masks using LI bootstrap method from SPM12. RS language LI was assessed using two novel methods of calculating RS language LI from bilateral Broca's area seed based connectivity maps across regional masks and multiple thresholds (p < .05, p < .01, p < .001, top 10% connections). We compared LI from task and RS fMRI continuous values and dominance classifications. We found significant positive correlations between task LI and RS LI when functional connectivity thresholds were set to the top 10% of connections. Concordance of dominance classifications ranged from 20% to 30% for the intrahemispheric resting state LI method and 50% to 63% for the resting state LI intra‐ minus interhemispheric difference method. Approximately 40% of patients left dominant on task showed RS bilateral dominance. There was no difference in LI concordance between patients with right‐sided and left‐sided resections. Early seizure onset (<6 years old) was not associated with atypical language dominance during task‐based or RS fMRI. While a relationship between task LI and RS LI exists in patients with epilepsy, language dominance is less lateralized on RS than task fMRI. Concordance of language dominance classifications between task and resting state fMRI depends on brain regions surveyed and RS LI calculation method.

Functional Networks in Epilepsy Presurgical Evaluation

Continuing advancements in neuroimaging methodology allow for increasingly detailed in vivo characterization of structural and functional brain networks, leading to the recognition of epilepsy as a disorder of large-scale networks. In surgical candidates, analysis of functional networks has proved invaluable for the identification of eloquent brain areas, such as hemispherical language dominance. More recently, connectome-based biomarkers have demonstrated potential to further inform clinical decision making in drug-refractory epilepsy. This article summarizes current evidence on epilepsy as a network disorder, emphasizing potential benefits of network analysis techniques for preoperative assessments and resection planning.

Thoughts of death affect reward learning by modulating salience network activity

Thoughts of death substantially influence human behavior and psychological well-being. A large number of behavioral studies have shown evidence that asking individuals to think about death or mortality salience leads to significant changes of their behaviors. These findings support the well-known terror management theory to account for the psychological mechanisms of existential anxiety. However, despite increasing findings of mortality salience effects on human behavior, how the brain responds to reminders of mortality and changes the activity underlying subsequent behavior remains poorly understood. By scanning healthy adults (N = 80) of both sexes using functional magnetic resonance imaging, we showed that, relative to reading emotionally neutral sentences, reading sentences that evoke death-related thoughts decreased the salience network activity, reduced the connectivity between the cingulate cortex and other brain regions during a subsequent resting state, and dampened the speed of learning reward-related objects and cingulate responses to loss feedback during a subsequent reward learning task. In addition, the decreased resting-state cingulate connectivity mediated the association between salience network deactivations in response to reminders of mortality and suppressed cingulate responses to loss feedback. Finally, the suppressed cingulate responses to loss feedback further predicted the dampened speed of reward learning. Our findings demonstrate sequential modulations of the salience network activity by mortality salience, which provide a neural basis for understanding human behavior under mortality threat.

Neuronal network models of epileptogenesis

Epilepsy is a chronic neurological condition, following some trigger, transforming a normal brain to one that produces recurrent unprovoked seizures. In the search for the mechanisms that best explain the epileptogenic process, there is a growing body of evidence suggesting that the epilepsies are network level disorders. In this review, we briefly describe the concept of neuronal networks and highlight 2 methods used to analyse such networks. The first method, graph theory, is used to describe general characteristics of a network to facilitate comparison between normal and abnormal networks. The second, dynamic causal modelling, is useful in the analysis of the pathways of seizure spread. We concluded that the end results of the epileptogenic process are best understood as abnormalities of neuronal circuitry and not simply as molecular or cellular abnormalities. The network approach promises to generate new understanding and more targeted treatment of epilepsy.

Neuroimaging correlates of language network impairment and reorganization in temporal lobe epilepsy

Advanced, noninvasive imaging has revolutionized our understanding of language networks in the brain and is reshaping our approach to the presurgical evaluation of patients with epilepsy. Functional magnetic resonance imaging (fMRI) has had the greatest impact, unveiling the complexity of language organization and reorganization in patients with epilepsy both pre- and postoperatively, while volumetric MRI and diffusion tensor imaging have led to a greater appreciation of structural and microstructural correlates of language dysfunction in different epilepsy syndromes. In this article, we review recent literature describing how unimodal and multimodal imaging has advanced our knowledge of language networks and their plasticity in epilepsy, with a focus on the most frequently studied epilepsy syndrome in adults, temporal lobe epilepsy (TLE). We also describe how new analytic techniques (i.e., graph theory) are leading to a refined characterization of abnormal brain connectivity, and how subject-specific imaging profiles combined with clinical data may enhance the prediction of both seizure and language outcomes following surgical interventions.

Node centrality measures are a poor substitute for causal inference

Network models have become a valuable tool in making sense of a diverse range of social, biological, and information systems. These models marry graph and probability theory to visualize, understand, and interpret variables and their relations as nodes and edges in a graph. Many applications of network models rely on undirected graphs in which the absence of an edge between two nodes encodes conditional independence between the corresponding variables. To gauge the importance of nodes in such a network, various node centrality measures have become widely used, especially in psychology and neuroscience. It is intuitive to interpret nodes with high centrality measures as being important in a causal sense. Using the causal framework based on directed acyclic graphs (DAGs), we show that the relation between causal influence and node centrality measures is not straightforward. In particular, the correlation between causal influence and several node centrality measures is weak, except for eigenvector centrality. Our results provide a cautionary tale: if the underlying real-world system can be modeled as a DAG, but researchers interpret nodes with high centrality as causally important, then this may result in sub-optimal interventions.

A Magnetoencephalography Study of Pediatric Interictal Neuromagnetic Activity Changes and Brain Network Alterations Caused by Epilepsy in the High Frequency (80-1000 Hz)

More and more studies propose that high frequency brain signals are promising biomarkers of epileptogenic zone. In this paper, our aim is to investigate the neuromagnetic changes and brain network topological alterations during an interictal period at high frequency ranges (80-1000 Hz) between healthy controls and epileptic patients with Magnetoencephalography. We analyzed neuromagnetic activities with accumulated source imaging, and constructed brain network based on graph theory. Neuromagnetic activity changes and brain network alterations between two groups were analyzed in three frequency bands: ripple (80-250 Hz), fast ripples (FRs, 250-500 Hz), and very high frequency oscillations (VHFO, 500-1000 Hz). We found that epileptic patients showed significantly altered patterns of neuromagnetic source localization and altered brain network patterns. And, we also found that mean functional connectivity and the number of modules from epileptic patients significantly increased in the ripple and FRs bands, and mean clustering coefficient from epileptic patients significantly decreased in the ripple and FRs bands. We also found that the mean functional connectivity was positively correlated with duration of epilepsy in the ripple and VHFO bands, and the number of modules was positively correlated with the duration of epilepsy in the ripple, FRs, and VHFO bands. Our results indicate that epilepsy can alter patients' neuromagnetic activities and brain networks in the high-frequency ranges, and these alterations become more pathological as the duration of epilepsy grows longer.

Characterization of brain network supporting episodic memory in the absence of one medial temporal lobe

How the brain supports normal episodic memory function without medial temporal lobe (MTL) structures has not been well characterized, which could provide clues for new therapeutic targets for people with MTL dysfunction‐related memory impairment. To characterize brain network supporting effective episodic memory function in the absence of unilateral MTL, we investigated the whole‐brain cortical interactions during functional magnetic resonance imaging memory encoding paradigms of words and figures in patients who showed a normal range of memory capacity following unilateral MTL resection and healthy controls (HC). Compared to the HC, the patients showed less activation in the left inferior frontal areas and right thalamus together with greater activation in the many cortical areas including the medial prefrontal cortex (mPFC). Task‐based functional connectivity (FC) analysis revealed that the mPFC showed stronger interactions with widespread brain areas in both patient groups, including the hippocampus contralateral to the resection. Moreover, the strength of the mPFC FC predicts the individual memory capacity of the patients. Our data suggest that hyperconnectivity of distributed brain areas, especially the mPFC, is a neural mechanism for memory function in the absence of one MTL.

Baseline brain structural and functional predictors of clinical outcome in the early course of schizophrenia

Although schizophrenia is considered a brain disorder, the role of brain organization for symptomatic improvement remains inadequately defined. We investigated the relationship between baseline brain morphology, resting-state network connectivity and clinical response after 24-weeks of antipsychotic treatment in patients with schizophrenia (n = 95) using integrated multivariate analyses. There was no significant association between clinical response and measures of cortical thickness (r = 0.37, p = 0.98) and subcortical volume (r = 0.56, p = 0.15). By contrast, we identified a strong mode of covariation linking functional network connectivity to clinical response (r = 0.70; p = 0.04), and particularly to improvement in positive (weight = 0.62) and anxious/depressive symptoms (weight = 0.49). Higher internal cohesiveness of the default mode network was the single most important positive predictor. Key negative predictors involved the functional cohesiveness of central executive subnetworks anchored in the frontoparietal cortices and subcortical regions (including the thalamus and striatum) and the inter-network integration between the default mode and sensorimotor networks. The present findings establish links between clinical response and the functional organization of brain networks involved both in perception and in spontaneous and goal-directed cognition, thereby advancing our understanding of the pathophysiology of schizophrenia.

Language network measures at rest indicate individual differences in naming decline after anterior temporal lobe resection

While anterior temporal lobe (ATL) resection is an effective treatment for temporal lobe epilepsy, surgery on the dominant hemisphere is associated with variable decline in confrontation naming. Accurate prediction of naming impairment is critical to inform clinical decision making, and while there has been some degree of success using task-based functional MRI (fMRI) paradigms, there remains a growing interest in the predictive utility of resting-state connectivity as it allows for relatively shorter scans with low task demands. Our objective was to assess the relationship between measures of preoperative resting-state connectivity and postoperative naming change in patients following left ATL resection. We compared the resting language network connectivity of each patient to a normative healthy control template using a novel measure called "matrix similarity," and found that patients with more abnormal global language-network connectivity-particularly of regions spared from surgery-showed greater postoperative naming decline than those with normative patterns of connectivity. When we interrogated the degree centrality of to-be-resected regions in a more targeted approach of the pathological temporal lobe, we found that greater functional integration of those regions with the rest of the language network at rest was related to greater decline in naming following surgery. Finally, we found that matrix similarity was a better predictor of postoperative outcome than degree within to-be-resected regions, network clustering, modularity, and language task fMRI laterality. We provide some of the first evidence that using this novel measure, a relatively short preoperative resting scan can be exploited to inform naming ability following ATL resection.

Consciousness in a multilevel architecture: Evidence from the right side of the brain

By taking into account Bruce Bridgeman's interest in an evolutionary framing of human cognition, we examine effective (cause-and-effect) connectivity among cortical structures related to different parts of the triune phylogenetic stratification: archicortex, paleocortex and neocortex. Using resting-state functional magnetic resonance imaging data from 25 healthy subjects and spectral Dynamic Causal Modeling, we report interactions among 10 symmetrical left and right brain areas. Our results testify to general rightward and top-down biases in excitatory interactions of these structures during resting state, when self-related contemplation prevails over more objectified conceptual thinking. The right hippocampus is the only structure that shows bottom-up excitatory influences extending to the frontopolar cortex. The right ventrolateral cortex also plays a prominent role as it interacts with the majority of nodes within and between evolutionary distinct brain subdivisions. These results suggest the existence of several levels of cognitive-affective organization in the human brain and their profound lateralization.

Cognition and connectomes in nondementia idiopathic Parkinson's disease

In this study, we investigate the organization of the structural connectome in cognitively well participants with Parkinson’s disease (PD-Well; n = 31) and a subgroup of participants with Parkinson’s disease who have amnestic disturbances (PD-MI; n = 9). We explore correlations between connectome topology and vulnerable cognitive domains in Parkinson’s disease relative to non-Parkinson’s disease peers (control, n = 40). Diffusion-weighted MRI data and deterministic tractography were used to generate connectomes. Connectome topological indices under study included weighted indices of node strength, path length, clustering coefficient, and small-worldness. Relative to controls, node strength was reduced 4.99% for PD-Well (p = 0.041) and 13.2% for PD-MI (p = 0.004). We found bilateral differences in the node strength between PD-MI and controls for inferior parietal, caudal middle frontal, posterior cingulate, precentral, and rostral middle frontal. Correlations between connectome and cognitive domains of interest showed that topological indices of global connectivity negatively associated with working memory and displayed more and larger negative correlations with neuropsychological indices of memory in PD-MI than in PD-Well and controls. These findings suggest that indices of network connectivity are reduced in PD-MI relative to PD-Well and control participants.

Parkinson’s disease (PD) patients with amnestic mild cognitive impairment (e.g., primary processing-speed impairments or primary memory impairments) are at greater risk of developing dementia. Recent evidence suggests that patients with PD and mild cognitive impairment present an altered connectome connectivity. In this work, we further explore the structural connectome of PD patients to provide clues to identify possible sensitive markers of disease progression, and cognitive impairment, in susceptible PD patients. We employed a weighted network framework that yields more stable topological results than the binary network framework and is robust despite graph density differences, hence it does not require thresholding to analyze the connectomes. As Supplementary Information (Colon-Perez et al., 2017), we include databases sharing the results of the network data.

Multimodal connectivity based eloquence score computation and visualisation for computer-aided neurosurgical path planning

Non-invasive assessment of cognitive importance has been a major challenge for planning of neurosurgical procedures. In the past decade, in vivo brain imaging modalities have been considered for estimating the ‘eloquence’ of brain areas. In order to estimate the impact of damage caused by an access path towards a target region inside of the skull, multi-modal metrics are introduced in this paper. Accordingly, this estimated damage is obtained by combining multi-modal metrics. In other words, this damage is an aggregate of intervened grey matter volume and axonal fibre numbers, weighted by their importance within the assigned anatomical and functional networks. To validate these metrics, an exhaustive search algorithm is implemented for characterising the solution space and visually representing connectional cost associated with a path initiated from underlying points. In this presentation, brain networks are built from resting state functional magnetic resonance imaging (fMRI) and deterministic tractography. their results demonstrate that the proposed approach is capable of refining traditional heuristics, such as choosing the minimal distance from the lesion, by supplementing connectional importance of the resected tissue. This provides complementary information to help the surgeon in avoiding important functional hubs and their anatomical linkages; which are derived from neuroimaging modalities and incorporated to the related anatomical landmarks.

Functional connectivity disturbances of the ascending reticular activating system in temporal lobe epilepsy

OBJECTIVE

Seizures in temporal lobe epilepsy (TLE) disturb brain networks and lead to connectivity disturbances. We previously hypothesised that recurrent seizures in TLE may lead to abnormal connections involving subcortical activating structures including the ascending reticular activating system (ARAS), contributing to neocortical dysfunction and neurocognitive impairments. However, no studies of ARAS connectivity have been previously reported in patients with epilepsy.

METHODS

We used resting-state functional MRI recordings in 27 patients with TLE (67% right sided) and 27 matched controls to examine functional connectivity (partial correlation) between eight brainstem ARAS structures and 105 cortical/subcortical regions. ARAS nuclei included: cuneiform/subcuneiform, dorsal raphe, locus coeruleus, median raphe, parabrachial complex, pontine oralis, pedunculopontine and ventral tegmental area. Connectivity patterns were related to disease and neuropsychological parameters.

RESULTS

In control subjects, regions showing highest connectivity to ARAS structures included limbic structures, thalamus and certain neocortical areas, which is consistent with prior studies of ARAS projections. Overall, ARAS connectivity was significantly lower in patients with TLE than controls (p<0.05, paired t-test), particularly to neocortical regions including insular, lateral frontal, posterior temporal and opercular cortex. Diminished ARAS connectivity to these regions was related to increased frequency of consciousness-impairing seizures (p<0.01, Pearson's correlation) and was associated with impairments in verbal IQ, attention, executive function, language and visuospatial memory on neuropsychological evaluation (p<0.05, Spearman's rho or Kendell's tau-b).

CONCLUSIONS

Recurrent seizures in TLE are associated with disturbances in ARAS connectivity, which are part of the widespread network dysfunction that may be related to neurocognitive problems in this devastating disorder.

Paradigm Shifts in the Neuropsychology of Epilepsy

This article reviews the major paradigm shifts that have occurred in the area of the application of clinical and experimental neuropsychology to epilepsy and epilepsy surgery since the founding of the International Neuropsychological Society. The five paradigm shifts discussed include: 1) The neurobiology of cognitive disorders in epilepsy - expanding the landscape of syndrome-specific neuropsychological impairment; 2) pathways to comorbidities: bidirectional relationships and their clinical implications; 3) discovering quality of life: The concept, its quantification and applicability; 4) outcomes of epilepsy surgery: challenging conventional wisdom; and 5) Iatrogenic effects of treatment: cognitive and behavioral effects of antiepilepsy drugs. For each area we characterize the status of knowledge, the key developments that have occurred, and how they have altered our understanding of the epilepsies and their management. We conclude with a brief overview of where we believe the field will be headed in the next decade which includes changes in assessment paradigms, moving from characterization of comorbidities to interventions; increasing development of new measures, terminology and classification; increasing interest in neurodegenerative proteins; transitioning from clinical seizure features to modifiable risk factors; and neurobehavioral phenotypes. Overall, enormous progress has been made over the lifespan of the INS with promise of ongoing improvements in understanding of the cognitive and behavioral complications of the epilepsies and their treatment. (JINS, 2017, 23, 791-805).

Neuropsychological outcomes following stereotactic laser amygdalohippocampectomy

OBJECTIVE

The objective was to analyze neuropsychological testing data from 15 patients before and after stereotactic laser ablation surgery for temporal lobe epilepsy and to describe the seizure outcomes after stereotactic laser ablation surgery.

METHODS

A retrospective review of 15 patients who underwent stereotactic laser ablation and who also underwent neuropsychological testing before and after surgery was performed. Verbal and visual memory was assessed in all 15 patients using California Verbal Learning Test and Wechsler Memory Scale IV. Naming was assessed in 9 of 15 patients using the Boston Naming Test. Statistical analysis was performed to determine clinically significant changes using previously validated reliable change indices and proprietary Advanced Clinical Solutions software. Seizure outcome data were evaluated using Engel classification.

RESULTS

Postsurgery neuropsychological evaluation demonstrated that all 15 patients experienced at least 1 clinically significant decline in either verbal or visual memory. Ten patients in this series, including five with dominant-hemisphere surgery, demonstrated decline in delayed memory for narrative information (Logical Memory II). By contrast, the Boston Naming Test demonstrated more favorable results after surgery. Two of nine patients demonstrated a clinically significant increase in naming ability, and only one of nine patients demonstrated a clinically significant decline in naming ability. With at least 6months of follow-up after surgery, 33% reported seizure freedom.

CONCLUSION

Stereotactic laser ablation can result in clinically significant and meaningful decline in verbal and visual memory when comparing patients to their own presurgical baseline. Naming ability, conversely, is much less likely to be impacted by stereotactic laser ablation and may improve after the procedure.

Structural brain network analysis of children with localization-related epilepsy

INTRODUCTION

Epilepsy is considered to arise from dysfunction in neural networks. Recent advances in neuroimaging and its analysis have made it possible to investigate both functional and structural connectivity in the brain. The aim of this study was to elucidate alterations in the structural connectivity in children with localization-related epilepsy using the mathematical method of graph theoretical analysis.

METHODOLOGY

Fifteen children with localization-related epilepsy (8 female subjects; mean age, 8.5±3.5years) as an epilepsy group and 23 children without a history of seizure (12 female subjects; mean age, 8.9±3.7years) as a control group underwent three-dimensional T1-weighted brain magnetic resonance imaging (MRI). Gray matter images segmented and spatially normalized from the MRIs of both groups were analyzed using statistical parametric mapping with the Graph Analysis Toolbox. We compared global networks (global efficiency, clustering coefficient and network strength) and regional networks (betweenness centrality and clustering) between patients and controls.

RESULTS

The global efficiency tended to be increased (p=0.081) and the global modularity was significantly increased (p=0.017) in the epilepsy group as compared with the control group. The epilepsy group showed locally decreased betweenness centrality mainly in the bilateral cingulate gyri, right perisylvian area, and bilateral precentral gyri, and locally increased clustering in the bilateral cingulate gyri, right perisylvian area, and medial frontal lobes as compared with the control group. The epilepsy group showed higher network resilience to random attack and targeted attack than the control group. Voxel-based morphometry did not show any difference between the two groups.

CONCLUSIONS

We observed globally increased structural connectivity along with excessive network robustness in patients with localization-related epilepsy. Local abnormality of connectivity was observed mainly in the cingulate gyrus, perisylvian area, and precentral gyrus. This alteration in the structural connectivity without any morphometric changes may be related to the underlying epileptogenicity.

Postoperative seizure freedom does not normalize altered connectivity in temporal lobe epilepsy

OBJECTIVES

Specific changes in the functional connectivity of brain networks occur in patients with epilepsy. Yet whether such changes reflect a stable disease effect or one that is a function of active seizure burden remains unclear. Here, we longitudinally assessed the connectivity of canonical cognitive functional networks in patients with intractable temporal lobe epilepsy (TLE), both before and after patients underwent epilepsy surgery and achieved seizure freedom.

METHODS

Seventeen patients with intractable TLE who underwent epilepsy surgery with Engel class I outcome and 17 matched healthy controls took part in the study. The functional connectivity of a set of cognitive functional networks derived from typical cognitive tasks was assessed in patients, preoperatively and postoperatively, as well as in controls, using stringent methods of artifact reduction.

RESULTS

Preoperatively, functional networks in TLE patients differed significantly from healthy controls, with differences that largely, but not exclusively, involved the default mode and temporal/auditory subnetworks. However, undergoing epilepsy surgery and achieving seizure freedom did not lead to significant changes in network connectivity, with postoperative functional network abnormalities closely mirroring the preoperative state.

SIGNIFICANCE

This result argues for a stable chronic effect of the disease on brain connectivity, with changes that are largely "burned in" by the time a patient with intractable TLE undergoes epilepsy surgery, which typically occurs years after the initial diagnosis. The result has potential implications for the treatment of intractable epilepsy, suggesting that delaying surgical intervention that may achieve seizure freedom may lead to functional network changes that are no longer reversible by the time of epilepsy surgery.

Biological Relevance of Network Architecture

Mathematical representations of brain networks in neuroscience through the use of graph theory may be very useful for the understanding of neurological diseases and disorders and such an explanatory power is currently under intense investigation. Graph metrics are expected to vary across subjects and are likely to reflect behavioural and cognitive performances. The challenge is to set up a framework that can explain how behaviour, cognition, memory, and other brain properties can emerge through the combined interactions of neurons, ensembles of neurons, and larger-scale brain regions that make information transfer possible. "Hidden" graph theoretic properties in the construction of brain networks may limit or enhance brain functionality and may be representative of aspects of human psychology. As theorems emerge from simple mathematical properties of graphs, similarly, cognition and behaviour may emerge from the molecular, cellular and brain region substrate interactions. In this review report, we identify some studies in the current literature that have used graph theoretical metrics to extract neurobiological conclusions, we briefly discuss the link with the human connectome project as an effort to integrate human data that may aid the study of emergent patterns and we suggest a way to start categorizing diseases according to their brain network pathologies as these are measured by graph theory.

Cognitive eloquence in neurosurgery: Insight from graph theoretical analysis of complex brain networks

The structure and function of the brain can be described by complex network models, and the topological properties of these models can be quantified by graph theoretical analysis. This has given insight into brain regions, known as hubs, which are critical for integrative functioning and information transfer, both fundamental aspects of cognition. In this manuscript a hypothesis is put forward for the concept of cognitive eloquence in neurosurgery; that is regions (cortical, subcortical and white matter) of the brain which may not necessarily have readily identifiable neurological function, but if injured may result in disproportionate cognitive morbidity. To this end, the effects of neurosurgical resection on cognition is reviewed and an overview of the role of complex network analysis in the understanding of brain structure and function is provided. The literature describing network, behavioral, and cognitive effects resulting from lesions to, and disconnections of, centralized hub regions will be emphasized as evidence for the espousal of the concept of cognitive eloquence.

Regional and global connectivity disturbances in focal epilepsy, related neurocognitive sequelae, and potential mechanistic underpinnings

Epilepsy is among the most common brain network disorders, and it is associated with substantial morbidity and increased mortality. Although focal epilepsy was traditionally considered a regional brain disorder, growing evidence has demonstrated widespread network alterations in this disorder that extend beyond the epileptogenic zone from which seizures originate. The goal of this review is to summarize recent investigations examining functional and structural connectivity alterations in focal epilepsy, including neuroimaging and electrophysiologic studies utilizing model-based or data-driven analytic methods. A significant subset of studies in both mesial temporal lobe epilepsy and focal neocortical epilepsy have demonstrated patterns of increased connectivity related to the epileptogenic zone, coupled with decreased connectivity in widespread distal networks. Connectivity patterns appear to be related to the duration and severity of disease, suggesting progressive connectivity reorganization in the setting of recurrent seizures over time. Global resting-state connectivity disturbances in focal epilepsy have been linked to neurocognitive problems, including memory and language disturbances. Although it is possible that increased connectivity in a particular brain region may enhance the propensity for seizure generation, it is not clear if global reductions in connectivity represent the damaging consequences of recurrent seizures, or an adaptive mechanism to prevent seizure propagation away from the epileptogenic zone. Overall, studying the connectome in focal epilepsy is a critical endeavor that may lead to improved strategies for epileptogenic-zone localization, surgical outcome prediction, and a better understanding of the neuropsychological implications of recurrent seizures.

Brain networks under attack: robustness properties and the impact of lesions

A growing number of studies approach the brain as a complex network, the so-called 'connectome'. Adopting this framework, we examine what types or extent of damage the brain can withstand-referred to as network 'robustness'-and conversely, which kind of distortions can be expected after brain lesions. To this end, we review computational lesion studies and empirical studies investigating network alterations in brain tumour, stroke and traumatic brain injury patients. Common to these three types of focal injury is that there is no unequivocal relationship between the anatomical lesion site and its topological characteristics within the brain network. Furthermore, large-scale network effects of these focal lesions are compared to those of a widely studied multifocal neurodegenerative disorder, Alzheimer's disease, in which central parts of the connectome are preferentially affected. Results indicate that human brain networks are remarkably resilient to different types of lesions, compared to other types of complex networks such as random or scale-free networks. However, lesion effects have been found to depend critically on the topological position of the lesion. In particular, damage to network hub regions-and especially those connecting different subnetworks-was found to cause the largest disturbances in network organization. Regardless of lesion location, evidence from empirical and computational lesion studies shows that lesions cause significant alterations in global network topology. The direction of these changes though remains to be elucidated. Encouragingly, both empirical and modelling studies have indicated that after focal damage, the connectome carries the potential to recover at least to some extent, with normalization of graph metrics being related to improved behavioural and cognitive functioning. To conclude, we highlight possible clinical implications of these findings, point out several methodological limitations that pertain to the study of brain diseases adopting a network approach, and provide suggestions for future research.

Functional substrate for memory function differences between patients with left and right mesial temporal lobe epilepsy associated with hippocampal sclerosis

OBJECTIVE

Little is known about the functional substrate for memory function differences in patients with left or right mesial temporal lobe epilepsy (mTLE) associated with hippocampal sclerosis (HS) from an electrophysiological perspective. To characterize these differences, we hypothesized that hippocampal theta connectivity in the resting-state might be different between patients with left and right mTLE with HS and be correlated with memory performance.

METHODS

Resting-state hippocampal theta connectivity, identified via whole-brain magnetoencephalography, was evaluated. Connectivity and memory function in 41 patients with mTLE with HS (left mTLE=22; right mTLE=19) were compared with those in 46 age-matched healthy controls and 28 patients with focal cortical dysplasia (FCD) but without HS.

RESULTS

Connectivity between the right hippocampus and the left middle frontal gyrus was significantly stronger in patients with right mTLE than in patients with left mTLE. Moreover, this connectivity was positively correlated with delayed verbal recall and recognition scores in patients with mTLE. Patients with left mTLE had greater delayed recall impairment than patients with right mTLE and FCD. Similarly, delayed recognition performance was worse in patients with left mTLE than in patients with right mTLE and FCD. No significant differences in memory function between patients with right mTLE and FCD were detected. Patients with right mTLE showed significantly stronger hippocampal theta connectivity between the right hippocampus and left middle frontal gyrus than patients with FCD and left mTLE.

CONCLUSION

Our results suggest that right hippocampal-left middle frontal theta connectivity could be a functional substrate that can account for differences in memory function between patients with left and right mTLE. This functional substrate might be related to different compensatory mechanisms against the structural hippocampal lesions in left and right mTLE groups. Given the positive correlation between connectivity and delayed verbal memory function, hemispheric-specific hippocampal-frontal theta connectivity assessment could be useful as an electrophysiological indicator of delayed verbal memory function in patients with mTLE with HS.