Network Connectivity in Epilepsy: Resting State fMRI and EEG-fMRI Contributions

Magnetic resonance imaging in canine idiopathic epilepsy: a mini-review

Magnetic resonance imaging (MRI) in an integral part of the diagnostic workup in canines with idiopathic epilepsy (IE). While highly sensitive and specific in identifying structural lesions, conventional MRI is unable to detect changes at the microscopic level. Utilizing more advanced neuroimaging techniques may provide further information on changes at the neuronal level in the brain of canines with IE, thus providing crucial information on the pathogenesis of canine epilepsy. Additionally, earlier detection of these changes may aid clinicians in the development of improved and targeted therapies. Advances in MRI techniques are being developed which can assess metabolic, cellular, architectural, and functional alterations; as well alterations in neuronal tissue mechanical properties, some of which are currently being applied in research on canine IE. This mini-review focuses on novel MRI techniques being utilized to better understand canine epilepsy, which include magnetic resonance spectroscopy, diffusion-weighted imaging, diffusion tensor imaging, perfusion-weighted imaging, voxel based morphometry, and functional MRI; as well as techniques applied in human medicine and their potential use in veterinary species.

Exploring Kainic Acid-Induced Alterations in Circular Tripartite Networks with Advanced Analysis Tools

Brain activity implies the orchestrated functioning of interconnected brain regions. Typical in vitro models aim to mimic the brain using single human pluripotent stem cell-derived neuronal networks. However, the field is constantly evolving to model brain functions more accurately through the use of new paradigms, e.g., brain-on-a-chip models with compartmentalized structures and integrated sensors. These methods create novel data requiring more complex analysis approaches. The previously introduced circular tripartite network concept models the connectivity between spatially diverse neuronal structures. The model consists of a microfluidic device allowing axonal connectivity between separated neuronal networks with an embedded microelectrode array to record both local and global electrophysiological activity patterns in the closed circuitry. The existing tools are suboptimal for the analysis of the data produced with this model. Here, we introduce advanced tools for synchronization and functional connectivity assessment. We used our custom-designed analysis to assess the interrelations between the kainic acid (KA)-exposed proximal compartment and its nonexposed distal neighbors before and after KA. Novel multilevel circuitry bursting patterns were detected and analyzed in parallel with the inter- and intracompartmental functional connectivity. The effect of KA on the proximal compartment was captured, and the spread of this effect to the nonexposed distal compartments was revealed. KA induced divergent changes in bursting behaviors, which may be explained by distinct baseline activity and varied intra- and intercompartmental connectivity strengths. The circular tripartite network concept combined with our developed analysis advances importantly both face and construct validity in modeling human epilepsy in vitro.

Altered correlation of concurrently recorded EEG-fMRI connectomes in temporal lobe epilepsy

Whole-brain functional connectivity networks (connectomes) have been characterized at different scales in humans using EEG and fMRI. Multimodal epileptic networks have also been investigated, but the relationship between EEG and fMRI defined networks on a whole-brain scale is unclear. A unified multimodal connectome description, mapping healthy and pathological networks would close this knowledge gap. Here, we characterize the spatial correlation between the EEG and fMRI connectomes in right and left temporal lobe epilepsy (rTLE/lTLE). From two centers, we acquired resting-state concurrent EEG-fMRI of 35 healthy controls and 34 TLE patients. EEG-fMRI data was projected into the Desikan brain atlas, and functional connectomes from both modalities were correlated. EEG and fMRI connectomes were moderately correlated. This correlation was increased in rTLE when compared to controls for EEG-delta/theta/alpha/beta. Conversely, multimodal correlation in lTLE was decreased in respect to controls for EEG-beta. While the alteration was global in rTLE, in lTLE it was locally linked to the default mode network. The increased multimodal correlation in rTLE and decreased correlation in lTLE suggests a modality-specific lateralized differential reorganization in TLE, which needs to be considered when comparing results from different modalities. Each modality provides distinct information, highlighting the benefit of multimodal assessment in epilepsy.

Atypical connectome topography and signal flow in temporal lobe epilepsy

Temporal lobe epilepsy (TLE) is the most common pharmaco-resistant epilepsy in adults. While primarily associated with mesiotemporal pathology, recent evidence suggests that brain alterations in TLE extend beyond the paralimbic epicenter and impact macroscale function and cognitive functions, particularly memory. Using connectome-wide manifold learning and generative models of effective connectivity, we examined functional topography and directional signal flow patterns between large-scale neural circuits in TLE at rest. Studying a multisite cohort of 95 patients with TLE and 95 healthy controls, we observed atypical functional topographies in the former group, characterized by reduced differentiation between sensory and transmodal association cortices, with most marked effects in bilateral temporo-limbic and ventromedial prefrontal cortices. These findings were consistent across all study sites, present in left and right lateralized patients, and validated in a subgroup of patients with histopathological validation of mesiotemporal sclerosis and post-surgical seizure freedom. Moreover, they were replicated in an independent cohort of 30 TLE patients and 40 healthy controls. Further analyses demonstrated that reduced differentiation related to decreased functional signal flow into and out of temporolimbic cortical systems and other brain networks. Parallel analyses of structural and diffusion-weighted MRI data revealed that topographic alterations were independent of TLE-related cortical thinning but partially mediated by white matter microstructural changes that radiated away from paralimbic circuits. Finally, we found a strong association between the degree of functional alterations and behavioral markers of memory dysfunction. Our work illustrates the complex landscape of macroscale functional imbalances in TLE, which can serve as intermediate markers bridging microstructural changes and cognitive impairment.

Brain activation and connection across resting and motor-task states in patients with generalized tonic-clonic seizures

AIMS

Motor abnormalities have been identified as one common symptom in patients with generalized tonic-clonic seizures (GTCS) inspiring us to explore the disease in a motor execution condition, which might provide novel insight into the pathomechanism.

METHODS

Resting-state and motor-task fMRI data were collected from 50 patients with GTCS, including 18 patients newly diagnosed without antiepileptic drugs (ND_GTCS) and 32 patients receiving antiepileptic drugs (AEDs_GTCS). Motor activation and its association with head motion and cerebral gradients were assessed. Whole-brain network connectivity across resting and motor states was further calculated and compared between groups.

RESULTS

All patients showed over-activation in the postcentral gyrus and the ND_GTCS showed decreased activation in putamen. Specifically, activation maps of ND_GTCS showed an abnormal correlation with head motion and cerebral gradient. Moreover, we detected altered functional network connectivity in patients within states and across resting and motor states by using repeated-measures analysis of variance. Patients did not show abnormal connectivity in the resting state, while distributed abnormal connectivity in the motor-task state. Decreased across-state network connectivity was also found in all patients.

CONCLUSION

Convergent findings suggested the over-response of activation and connection of the brain to motor execution in GTCS, providing new clues to uncover motor susceptibility underlying the disease.

Epilepsy-related functional brain network alterations are already present at an early age in the GAERS rat model of genetic absence epilepsy

Introduction

Genetic Absence Epilepsy Rats from Strasbourg (GAERS) represent a model of genetic generalized epilepsy. The present longitudinal study in GAERS and age-matched non-epileptic controls (NEC) aimed to characterize the epileptic brain network using two functional measures, resting state-functional magnetic resonance imaging (rs-fMRI) and manganese-enhanced MRI (MEMRI) combined with morphometry, and to investigate potential brain network alterations, following long-term seizure activity.

Methods

Repeated rs-fMRI measurements at 9.4 T between 3 and 8 months of age were combined with MEMRI at the final time point of the study. We used graph theory analysis to infer community structure and global and local network parameters from rs-fMRI data and compared them to brain region-wise manganese accumulation patterns and deformation-based morphometry (DBM).

Results

Functional connectivity (FC) was generally higher in GAERS when compared to NEC. Global network parameters and community structure were similar in NEC and GAERS, suggesting efficiently functioning networks in both strains. No progressive FC changes were observed in epileptic animals. Network-based statistics (NBS) revealed stronger FC within the cortical community, including regions of association and sensorimotor cortex, and with basal ganglia and limbic regions in GAERS, irrespective of age. Higher manganese accumulation in GAERS than in NEC was observed at 8 months of age, consistent with higher overall rs-FC, particularly in sensorimotor cortex and association cortex regions. Functional measures showed less similarity in subcortical regions. Whole brain volumes of 8 months-old GAERS were higher when compared to age-matched NEC, and DBM revealed increased volumes of several association and sensorimotor cortex regions and of the thalamus.

Discussion

rs-fMRI, MEMRI, and volumetric data collectively suggest the significance of cortical networks in GAERS, which correlates with an increased fronto-central connectivity in childhood absence epilepsy (CAE). Our findings also verify involvement of basal ganglia and limbic regions. Epilepsy-related network alterations are already present in juvenile animals. Consequently, this early condition seems to play a greater role in dynamic brain function than chronic absence seizures.

The time-evolving epileptic brain network: concepts, definitions, accomplishments, perspectives

Epilepsy is now considered a network disease that affects the brain across multiple levels of spatial and temporal scales. The paradigm shift from an epileptic focus-a discrete cortical area from which seizures originate-to a widespread epileptic network-spanning lobes and hemispheres-considerably advanced our understanding of epilepsy and continues to influence both research and clinical treatment of this multi-faceted high-impact neurological disorder. The epileptic network, however, is not static but evolves in time which requires novel approaches for an in-depth characterization. In this review, we discuss conceptual basics of network theory and critically examine state-of-the-art recording techniques and analysis tools used to assess and characterize a time-evolving human epileptic brain network. We give an account on current shortcomings and highlight potential developments towards an improved clinical management of epilepsy.

Altered functional connectivity after pilocarpine-induced seizures revealed by intrinsic optical signals imaging in awake mice

Significance

Comorbidities such as mood and cognitive disorders are often found in individuals with epilepsy after seizures. Cortex processes sensory, motor, and cognitive information. Brain circuit changes can be studied by observing functional network changes in epileptic mice's cortex.

Aim

The cortex is easily accessible for non-invasive brain imaging and electroencephalogram recording (EEG). However, the impact of seizures on cortical activity and functional connectivity has been rarely studied in vivo.

Approach

Intrinsic optical signal and EEG were used to monitor cortical activity in awake mice within 4 h after pilocarpine induction. It was divided into three periods according to the behavior and EEG of the mice: baseline, onset of seizures (onset, including seizures and resting in between seizure events), and after seizures (post, without seizures). Changes in cortical activity were compared between the baseline and after seizures.

Results

Hemoglobin levels increased significantly, particularly in the parietal association cortex (PT), retrosplenial cortex (RS), primary visual cortex (V1), and secondary visual cortex (V2). The network-wide functional connectivity changed post seizures, e.g., hypoconnectivity between PT and visual-associated cortex (e.g., V1 and V2). In contrast, connectivity between the motor-associated cortex and most other regions increased. In addition, the default mode network (DMN) also changed after seizures, with decreased connectivity between primary somatosensory region (SSp) and visual region (VIS), but increased connectivity involving anterior cingulate cortex (AC) and RS.

Conclusions

Our results provide references for understanding the mechanisms behind changes in brain circuits, which may explain the profound effects of seizures on comorbid health conditions.

Evolution of Stereo-Electroencephalography at Massachusetts General Hospital

The practice of invasive monitoring for presurgical epilepsy workup has evolved at Massachusetts General Hospital (MGH) in parallel to the evolution in the field's understanding of epilepsy as a network disorder. Implantations have shifted from an emphasis on singularly finding single foci for the purpose of resection to a network-hypothesis-driven approach aiming to delineate patients' seizure networks with the goal of developing surgical interventions that disrupt critical nodes of these networks. Here, the authors review all invasive monitoring cases at MGH from April 2016 through June 2023 to describe how this paradigm shift has taken form.

Aberrant dynamic functional network connectivity in type 2 diabetes mellitus individuals

An increasing number of recent brain imaging studies are dedicated to understanding the neuro mechanism of cognitive impairment in type 2 diabetes mellitus (T2DM) individuals. In contrast to efforts to date that are limited to static functional connectivity, here we investigate abnormal connectivity in T2DM individuals by characterizing the time-varying properties of brain functional networks. Using group independent component analysis (GICA), sliding-window analysis, and k-means clustering, we extracted thirty-one intrinsic connectivity networks (ICNs) and estimated four recurring brain states. We observed significant group differences in fraction time (FT) and mean dwell time (MDT), and significant negative correlation between the Montreal Cognitive Assessment (MoCA) scores and FT/MDT. We found that in the T2DM group the inter- and intra-network connectivity decreases and increases respectively for the default mode network (DMN) and task-positive network (TPN). We also found alteration in the precuneus network (PCUN) and enhanced connectivity between the salience network (SN) and the TPN. Our study provides evidence of alterations of large-scale resting networks in T2DM individuals and shed light on the fundamental mechanisms of neurocognitive deficits in T2DM.

Patients with epilepsy without cognitive impairment show altered brain networks in multiple frequency bands in an audiovisual integration task

OBJECTIVES

Comorbid cognitive and behavioral deficits are often observed in patients with epilepsy. It is not clear whether the brain networks of patients with epilepsy without cognitive decline differs from that of healthy controls in different frequency bands in the task-state. The purpose of our study was to explore whether epilepsy affects the structure of brain networks associated with cognitive processing, even when patients with epilepsy do not have cognitive impairment.

METHODS

We designed an audiovisual discrimination task and recorded electroencephalogram (EEG) data from healthy controls and patients with epilepsy. We established constructed time-varying brain networks across the delta, theta, alpha, and beta bands on the task-state EEG data during audiovisual integration processing.

RESULTS

The results showed changes in the structure of the brain networks in the theta, alpha, and beta bands in patients with epilepsy who had no cognitive deficit. No significant difference in the connectivity strength, clustering coefficient, characteristic path length, or global efficiency was noted between patients and healthy controls. Moreover, the structure of brain networks in patients showed no correlation with the behavioral performance.

CONCLUSION

The repeated abnormal firing of neurons in the brain of patients with epilepsy may inhibit it from optimizing networks into more efficient structures. Epilepsy might affect decision-making ability by damaging the neural activity in the beta band and preventing its correlation with decision-making behaviors.

Single neurons on microelectrode array chip: manipulation and analyses

Chips-based platforms intended for single-cell manipulation are considered powerful tools to analyze intercellular interactions and cellular functions. Although the conventional cell co-culture models could investigate cell communication to some extent, the role of a single cell requires further analysis. In this study, a precise intercellular interaction model was built using a microelectrode array [microelectrode array (MEA)]-based and dielectrophoresis-driven single-cell manipulation chip. The integrated platform enabled precise manipulation of single cells, which were either trapped on or transferred between electrodes. Each electrode was controlled independently to record the corresponding cellular electrophysiology. Multiple parameters were explored to investigate their effects on cell manipulation including the diameter and depth of microwells, the geometry of cells, and the voltage amplitude of the control signal. Under the optimized microenvironment, the chip was further evaluated using 293T and neural cells to investigate the influence of electric field on cells. An examination of the inappropriate use of electric fields on cells revealed the occurrence of oncosis. In the end of the study, electrophysiology of single neurons and network of neurons, both differentiated from human induced pluripotent stem cells (iPSC), was recorded and compared to demonstrate the functionality of the chip. The obtained preliminary results extended the nature growing model to the controllable level, satisfying the expectation of introducing more elaborated intercellular interaction models.

Cognitive dysfunction in drug-naïve late-onset temporal lobe epilepsy

OBJECTIVES

To evaluate cognitive functions including memory in middle-aged and elderly patients with antiseizure drug-naïve late-onset temporal lobe epilepsy (TLE).

METHODS

We performed assessments with the Wechsler Adult Intelligence Scale-III (WAIS-III) and Wechsler Memory Scale-Revised (WMS-R) in 26 antiseizure drug-naïve patients with late-onset TLE, in comparison to 30 healthy subjects. We investigated the relationships between these cognitive function scores and clinical characteristics, seizure frequency, and frequency of interictal epileptic discharges (IEDs).

RESULTS

Patients with epilepsy had a significantly lower score than healthy controls in the verbal intelligence quotient (IQ), the performance IQ, and full-scale IQ in intelligence testing. Patients showed significantly decrease in the verbal memory scores, visual memory scores, general memory scores, and delayed recall scores compared with those in the control subjects. Delayed recall scores were significantly negatively correlated with recent seizure frequency and the total IEDs count per minute, but not with age of onset or duration of illness.

SIGNIFICANCE

Patients with antiseizure drug-naïve late-onset TLE displayed cognitive deficits including the domains of memory by using standard clinical neuropsychological test. Patients with late-onset epilepsy need to be considered for cognitive dysfunction at the time of diagnosis of TLE because they may have their daily life and work affected not only by epileptic seizures but also by cognitive deficits. Appearance of seizures and EEG abnormalities may affect the memory function in patients with late-onset TLE.

Impaired rich-club connectivity in childhood absence epilepsy

Introduction

Childhood absence epilepsy (CAE) is a well-known pediatric epilepsy syndrome. Recent evidence has shown the presence of a disrupted structural brain network in CAE. However, little is known about the rich-club topology. This study aimed to explore the rich-club alterations in CAE and their association with clinical characteristics.

Methods

Diffusion tensor imaging (DTI) datasets were acquired in a sample of 30 CAE patients and 31 healthy controls. A structural network was derived from DTI data for each participant using probabilistic tractography. Then, the rich-club organization was examined, and the network connections were divided into rich-club connections, feeder connections, and local connections.

Results

Our results confirmed a less dense whole-brain structural network in CAE with lower network strength and global efficiency. In addition, the optimal organization of small-worldness was also damaged. A small number of highly connected and central brain regions were identified to form the rich-club organization in both patients and controls. However, patients exhibited a significantly reduced rich-club connectivity, while the other class of feeder and local connections was relatively spared. Moreover, the lower levels of rich-club connectivity strength were statistically correlated with disease duration.

Discussion

Our reports suggest that CAE is characterized by abnormal connectivity concentrated to rich-club organizations and might contribute to understanding the pathophysiological mechanism of CAE.

Neurocysticercosis-related Seizures: Imaging Biomarkers

Neurocysticercosis (NCC)-a parasitic CNS infection endemic to developing nations-has been called the leading global cause of acquired epilepsy yet remains understudied. It is currently unknown why a large proportion of patients develop recurrent seizures, often following the presentation of acute seizures. Furthermore, the presentation of NCC is heterogenous and the features that predispose to the development of an epileptogenic state remain uncertain. Perilesional factors (such as oedema and gliosis) have been implicated in NCC-related ictogenesis, but the effects of cystic factors, including lesion load and location, seem not to play a role in the development of habitual epilepsy. In addition, the cytotoxic consequences of the cyst's degenerative stages are varied and the majority of research, relying on retrospective data, lacks the necessary specificity to distinguish between acute symptomatic and unprovoked seizures. Previous research has established that epileptogenesis can be the consequence of abnormal network connectivity, and some imaging studies have suggested that a causative link may exist between NCC and aberrant network organisation. In wider epilepsy research, network approaches have been widely adopted; studies benefiting predominantly from the rich, multimodal data provided by advanced MRI methods are at the forefront of the field. Quantitative MRI approaches have the potential to elucidate the lesser-understood epileptogenic mechanisms of NCC. This review will summarise the current understanding of the relationship between NCC and epilepsy, with a focus on MRI methodologies. In addition, network neuroscience approaches with putative value will be highlighted, drawing from current imaging trends in epilepsy research.

Pre-surgical features of intrinsic brain networks predict single and joint epilepsy surgery outcomes

Despite the effectiveness of surgical interventions for the treatment of intractable focal temporal lobe epilepsy (TLE), the substrates that support good outcomes are poorly understood. While algorithms have been developed for the prediction of either seizure or cognitive/psychiatric outcomes alone, no study has reported on the functional and structural architecture that supports joint outcomes. We measured key aspects of pre-surgical whole brain functional/structural network architecture and evaluated their ability to predict post-operative seizure control in combination with cognitive/psychiatric outcomes. Pre-surgically, we identified the intrinsic connectivity networks (ICNs) unique to each person through independent component analysis (ICA), and computed: (1) the spatial-temporal match between each person's ICA components and established, canonical ICNs, (2) the connectivity strength within each identified person-specific ICN, (3) the gray matter (GM) volume underlying the person-specific ICNs, and (4) the amount of variance not explained by the canonical ICNs for each person. Post-surgical seizure control and reliable change indices of change (for language [naming, phonemic fluency], verbal episodic memory, and depression) served as binary outcome responses in random forest (RF) models. The above functional and structural measures served as input predictors. Our empirically derived ICN-based measures customized to the individual showed that good joint seizure and cognitive/psychiatric outcomes depended upon higher levels of brain reserve (GM volume) in specific networks. In contrast, singular outcomes relied on systematic, idiosyncratic variance in the case of seizure control, and the weakened pre-surgical presence of functional ICNs that encompassed the ictal temporal lobe in the case of cognitive/psychiatric outcomes. Our data made clear that the ICNs differed in their propensity to provide reserve for adaptive outcomes, with some providing structural (brain), and others functional (cognitive) reserve. Our customized methodology demonstrated that when substantial unique, patient-specific ICNs are present prior to surgery there is a reliable association with poor post-surgical seizure control. These ICNs are idiosyncratic in that they did not match the canonical, normative ICNs and, therefore, could not be defined functionally, with their location likely varying by patient. This important finding suggested the level of highly individualized ICN's in the epileptic brain may signal the emergence of epileptogenic activity after surgery.

Resting-State Functional MRI/PET Profile as a Potential Alternative to Tri-Modality EEG-MR/PET Imaging: An Exploratory Study in Drug-Refractory Epilepsy

Objective The study explores whether the epileptic networks associate with predetermined seizure onset zone (SOZ) identified from other modalities such as electroencephalogram/video electroencephalogram/structural MRI (EEG/VEEG/sMRI) and with the degree of resting-state functional MRI/positron emission tomography (RS-fMRI/PET) coupling. Here, we have analyzed the subgroup of patients who reported having a seizure on the day of scan as postictal cases and compared the findings with interictal cases (seizure-free interval).

Methods We performed independent component analysis (ICA) on RS-fMRI and 20 ICA were hand-labeled as large scale, noise, downstream, and epilepsy networks (Epinets) based on their profile in spatial, time series, and power spectrum domains. We had a total of 43 cases, with 4 cases in the postictal group (100%). Of 39 cases, 14 cases did not yield any Epinet and 25 cases (61%) were analyzed for the final study. The analysis was done patient-wise and correlated with predetermined SOZ.

Results The yield of finding Epinets on RS-fMRI is more during the postictal period than in the interictal period, although PET and RS-fMRI spatial, time series, and power spectral patterns were similar in both these subgroups. Overlaps between large-scale and downstream networks were noted, indicating that epilepsy propagation can involve large-scale cognition networks. Lateralization to SOZ was noted as blood oxygen level–dependent activation and correlated with sMRI/PET findings. Postoperative surgical failure cases showed residual Epinet profile.

Conclusion RS-fMRI may be a viable option for trimodality imaging to obtain simultaneous physiological information at the functional network and metabolic level.

Camera-based Prospective Motion Correction in Paediatric Epilepsy Patients Enables EEG-fMRI Localization Even in High-motion States

BACKGROUND

EEG-fMRI is a useful additional test to localize the epileptogenic zone (EZ) particularly in MRI negative cases. However subject motion presents a particular challenge owing to its large effects on both MRI and EEG signal. Traditionally it is assumed that prospective motion correction (PMC) of fMRI precludes EEG artifact correction.

METHODS

Children undergoing presurgical assessment at Great Ormond Street Hospital were included into the study. PMC of fMRI was done using a commercial system with a Moiré Phase Tracking marker and MR-compatible camera. For retrospective EEG correction both a standard and a motion educated EEG artefact correction (REEGMAS) were compared to each other.

RESULTS

Ten children underwent simultaneous EEG-fMRI. Overall head movement was high (mean RMS velocity < 1.5 mm/s) and showed high inter- and intra-individual variability. Comparing motion measured by the PMC camera and the (uncorrected residual) motion detected by realignment of fMRI images, there was a five-fold reduction in motion from its prospective correction. Retrospective EEG correction using both standard approaches and REEGMAS allowed the visualization and identification of physiological noise and epileptiform discharges. Seven of 10 children had significant maps, which were concordant with the clinical EZ hypothesis in 6 of these 7.

CONCLUSION

To our knowledge this is the first application of camera-based PMC for MRI in a pediatric clinical setting. Despite large amount of movement PMC in combination with retrospective EEG correction recovered data and obtained clinically meaningful results during high levels of subject motion. Practical limitations may currently limit the widespread use of this technology.

DeepEZ: A Graph Convolutional Network for Automated Epileptogenic Zone Localization From Resting-State fMRI Connectivity

OBJECTIVE

Epileptogenic zone (EZ) localization is a crucial step during diagnostic work up and therapeutic planning in medication refractory epilepsy. In this paper, we present the first deep learning approach to localize the EZ based on resting-state fMRI (rs-fMRI) data.

METHODS

Our network, called DeepEZ, uses a cascade of graph convolutions that emphasize signal propagation along expected anatomical pathways. We also integrate domain-specific information, such as an asymmetry term on the predicted EZ and a learned subject-specific bias to mitigate environmental confounds.

RESULTS

We validate DeepEZ on rs-fMRI collected from 14 patients with focal epilepsy at the University of Wisconsin Madison. Using cross validation, we demonstrate that DeepEZ achieves consistently high EZ localization performance (Accuracy: 0.88 ± 0.03; AUC: 0.73 ± 0.03) that far outstripped any of the baseline methods. This performance is notable given the variability in EZ locations and scanner type across the cohort.

CONCLUSION

Our results highlight the promise of using DeepEZ as an accurate and noninvasive therapeutic planning tool for medication refractory epilepsy.

SIGNIFICANCE

While prior work in EZ localization focused on identifying localized aberrant signatures, there is growing evidence that epileptic seizures affect inter-regional connectivity in the brain. DeepEZ allows clinicians to harness this information from noninvasive imaging that can easily be integrated into the existing clinical workflow.

Functional connectivity alterations in migraineurs with Alice in Wonderland syndrome

BACKGROUND AND PURPOSE

Alice in Wonderland syndrome (AIWS) is a neurological disorder characterized by erroneous perception of the body schema or surrounding space. Migraine is the primary cause of AIWS in adults. The pathophysiology of AIWS is largely unknown, especially regarding functional abnormalities. In this study, we compared resting-state functional connectivity (FC) of migraine patients experiencing AIWS, migraine patients with typical aura (MA) and healthy controls (HCs).

METHODS

Twelve AIWS, 12 MA, and 24 HCs were enrolled and underwent 3 T MRI scanning. Independent component analysis was used to identify RSNs thought to be relevant for AIWS: visual, salience, basal ganglia, default mode, and executive control networks. Dual regression technique was used to detect between-group differences in RSNs. Finally, AIWS-specific FC alterations were correlated with clinical measures.

RESULTS

With respect to HCs, AIWS and MA patients both showed significantly lower (p < 0.05, FDR corrected) FC in lateral and medial visual networks and higher FC in salience and default mode networks. AIWS patients alone showed higher FC in basal ganglia and executive control networks than HCs. When directly compared, AIWS patients showed lower FC in visual networks and higher FC in all other investigated RSNs than MA patients. Lastly, AIWS-specific FC alterations in the executive control network positively correlated with migraine frequency.

CONCLUSIONS

AIWS and MA patients showed similar FC alterations in several RSNs, although to a different extent, suggesting common pathophysiological underpinnings. However, AIWS patients showed additional FC alterations, likely due to the complexity of AIWS symptoms involving high-order associative cortical areas.

Towards network-guided neuromodulation for epilepsy

Epilepsy is well-recognized as a disorder of brain networks. There is a growing body of research to identify critical nodes within dynamic epileptic networks with the aim to target therapies that halt the onset and propagation of seizures. In parallel, intracranial neuromodulation, including deep brain stimulation and responsive neurostimulation, are well-established and expanding as therapies to reduce seizures in adults with focal-onset epilepsy; and there is emerging evidence for their efficacy in children and generalized-onset seizure disorders. The convergence of these advancing fields is driving an era of 'network-guided neuromodulation' for epilepsy. In this review, we distil the current literature on network mechanisms underlying neurostimulation for epilepsy. We discuss the modulation of key 'propagation points' in the epileptogenic network, focusing primarily on thalamic nuclei targeted in current clinical practice. These include (i) the anterior nucleus of thalamus, now a clinically approved and targeted site for open loop stimulation, and increasingly targeted for responsive neurostimulation; and (ii) the centromedian nucleus of the thalamus, a target for both deep brain stimulation and responsive neurostimulation in generalized-onset epilepsies. We discuss briefly the networks associated with other emerging neuromodulation targets, such as the pulvinar of the thalamus, piriform cortex, septal area, subthalamic nucleus, cerebellum and others. We report synergistic findings garnered from multiple modalities of investigation that have revealed structural and functional networks associated with these propagation points - including scalp and invasive EEG, and diffusion and functional MRI. We also report on intracranial recordings from implanted devices which provide us data on the dynamic networks we are aiming to modulate. Finally, we review the continuing evolution of network-guided neuromodulation for epilepsy to accelerate progress towards two translational goals: (i) to use pre-surgical network analyses to determine patient candidacy for neurostimulation for epilepsy by providing network biomarkers that predict efficacy; and (ii) to deliver precise, personalized and effective antiepileptic stimulation to prevent and arrest seizure propagation through mapping and modulation of each patients' individual epileptogenic networks.

Increased interictal synchronicity of respiratory related brain pulsations in epilepsy

Respiratory brain pulsations have recently been shown to drive electrophysiological brain activity in patients with epilepsy. Furthermore, functional neuroimaging indicates that respiratory brain pulsations have increased variability and amplitude in patients with epilepsy compared to healthy individuals. To determine whether the respiratory drive is altered in epilepsy, we compared respiratory brain pulsation synchronicity between healthy controls and patients. Whole brain fast functional magnetic resonance imaging was performed on 40 medicated patients with focal epilepsy, 20 drug-naïve patients and 102 healthy controls. Cerebrospinal fluid associated respiratory pulsations were used to generate individual whole brain respiratory synchronization maps, which were compared between groups. Finally, we analyzed the seizure frequency effect and diagnostic accuracy of the respiratory synchronization defect in epilepsy. Respiratory brain pulsations related to the verified fourth ventricle pulsations were significantly more synchronous in patients in frontal, periventricular and mid-temporal regions, while the seizure frequency correlated positively with synchronicity. The respiratory brain synchronicity had a good diagnostic accuracy (ROC_AUC = 0.75) in discriminating controls from medicated patients. The elevated respiratory brain synchronicity in focal epilepsy suggests altered physiological effect of cerebrospinal fluid pulsations possibly linked to regional brain water dynamics involved with interictal brain physiology.

Concordance of functional MRI memory task and resting-state functional MRI connectivity used in surgical planning for pediatric temporal lobe epilepsy

OBJECTIVE

Assessing memory is often critical in surgical evaluation, although difficult to assess in young children and in patients with variable task abilities. While obtaining interpretable data from task-based functional MRI (fMRI) measures is common in compliant and awake patients, it is not known whether functional connectivity MRI (fcMRI) data show equivalent results. If this were the case, it would have substantial clinical and research generalizability. To evaluate this possibility, the authors evaluated the concordance between fMRI and fcMRI data collected in a presurgical epilepsy cohort.

METHODS

Task-based fMRI data for autobiographical memory tasks and resting-state fcMRI data were collected in patients with epilepsy evaluated at Seattle Children's Hospital between 2010 and 2017. To assess memory-related activation and laterality, signal change in task-based measures was computed as a percentage of the average blood oxygen level-dependent signal over the defined regions of interest. An fcMRI data analysis was performed using 1000 Functional Connectomes Project scripts based on Analysis of Functional NeuroImages and FSL (Functional Magnetic Resonance Imaging of the Brain Software Library) software packages. Lateralization indices (LIs) were estimated for activation and connectivity measures. The concordance between these two measures was evaluated using correlation and regression analysis.

RESULTS

In this epilepsy cohort studied, the authors observed concordance between fMRI activation and fcMRI connectivity, with an LI regression coefficient of 0.470 (R2 = 0.221, p = 0.00076).

CONCLUSIONS

Previously published studies have demonstrated fMRI and fcMRI overlap between measures of vision, attention, and language. In the authors' clinical sample, task-based measures of memory and analogous resting-state mapping were similarly linked in pattern and strength. These results support the use of fcMRI methods as a proxy for task-based memory performance in presurgical patients, perhaps including those who are more limited in their behavioral compliance. Future investigations to extend these results will be helpful to explore how the magnitudes of effect are associated with neuropsychological performance and postsurgical behavioral changes.

Pairwise and higher-order measures of brain-heart interactions in children with temporal lobe epilepsy

OBJECTIVE

While it is well-known that epilepsy has a clear impact on the activity of both the central nervous system (CNS) and the autonomic nervous system (ANS), its role on the complex interplay between CNS and ANS has not been fully elucidated yet. In this work, pairwise and higher-order predictability measures based on the concepts of Granger causality (GC) and Partial Information Decomposition (PID) were applied on time series of electroencephalographic (EEG) brain wave amplitude and heart rate variability (HRV) in order to investigate directed brain-heart interactions associated with the occurrence of focal epilepsy.

APPROACH

HRV and the envelopes of δ and α EEG activity recorded from ipsilateral (ipsi-EEG) and contralateral (contra-EEG) scalp regions were analyzed in 18 children suffering from temporal lobe epilepsy monitored during pre-ictal, ictal and post-ictal periods. After linear parametric model identification, we compared pairwise GC measures computed between HRV and a single EEG component with PID measures quantifying the unique, redundant and synergistic information transferred from ipsi-EEG and contra-EEG to HRV.

MAIN RESULTS

The analysis of GC revealed a dominance of the information transfer from EEG to HRV and negligible transfer from HRV to EEG, suggesting that CNS activities drive the ANS modulation of the heart rhythm, but did not evidence clear differences between δ and α rhythms, ipsi-EEG and contra-EEG, or pre- and post-ictal periods. On the contrary, PID revealed that epileptic seizures induce a reorganization of the interactions from brain to heart, as the unique predictability of HRV originated from the ipsi-EEG for the δ waves and from the contra-EEG for the α waves in the pre-ictal phase, while these patterns were reversed after the seizure.

SIGNIFICANCE

These results highlight the importance of considering higher-order interactions elicited by PID for the study of the neuro-autonomic effects of focal epilepsy, and may have neurophysiological and clinical implications.

Light sedation with short habituation time for large-scale functional magnetic resonance imaging studies in rats

Traditionally, preclinical resting state functional magnetic resonance imaging (fMRI) studies have been performed in anesthetized animals. Nevertheless, as anesthesia affects the functional connectivity (FC) in the brain, there has been a growing interest in imaging in the awake state. Obviously, awake imaging requires resource- and time-consuming habituation prior to data acquisition to reduce the stress and motion of the animals. Light sedation has been a less widely exploited alternative for awake imaging, requiring shorter habituation times, while still reducing the effect of anesthesia. Here, we imaged 102 rats under light sedation and 10 awake animals to conduct an FC analysis. We established an automated data-processing pipeline suitable for both groups. Additionally, the same pipeline was used on data obtained from an openly available awake rat database (289 measurements in 90 rats). The FC pattern in the light sedation measurements closely resembled the corresponding patterns in both onsite and offsite awake datasets. However, fewer datasets had to be excluded due to movement in rats with light sedation. The temporal analysis of FC in the lightly sedated group indicated a lingering effect of anesthesia that stabilized after the first 5 min. In summary, our results indicate that the light sedation protocol is a valid alternative for large-scale studies where awake protocols may become prohibitively resource-demanding, as it provides similar results to awake imaging, preserves more scans, and requires shorter habituation times. The large amount of fMRI data obtained in this work are openly available for further analyses.

Gastrointestinal and Autonomic Symptoms—How to Improve the Diagnostic Process in Panayiotopoulos Syndrome?

One of the most common epileptic disorders in the pediatric population is Panayiotopoulos syndrome. Clinical manifestations of this idiopathic illness include predominantly autonomic symptoms and dysfunction of the cardiorespiratory system. Another feature constitutes prolonged seizures that usually occur at sleep. It is crucial to differentiate the aforementioned disease from other forms of epilepsy, especially occipital and structural epilepsy and non-epileptic disorders. The diagnostic process is based on medical history, clinical examination, neuroimaging and electroencephalography—though results of the latter may be unspecific. Patients with Panayiotopoulos syndrome (PS) do not usually require treatment, as the course of the disease is, in most cases, mild, and the prognosis is good. The purpose of this review is to underline the role of central autonomic network dysfunction in the development of Panayiotopoulos syndrome, as well as the possibility of using functional imaging techniques, especially functional magnetic resonance imaging (fMRI), in the diagnostic process. These methods could be crucial for understanding the pathogenesis of PS. More data arerequired to create algorithms that will be able to predict the exposure to various complications of PS. It also concerns the importance of electroencephalography (EEG) as a tool to distinguish Panayiotopoulos syndrome from other childhood epileptic syndromes and non-epileptic disorders.

Cohesive parcellation of the human brain using resting-state fMRI

BACKGROUND

The data burden for resting-state fMRI analysis rises with increasing resolutions available at ultrahigh fields. Therefore, a fundamental preprocessing step in brain network analysis is to reduce the data, usually by performing some kind of data parcellation. Most functional parcellations based on rsfMRI connectivity are synthesized from the dense connectome. In contrast, most network analyses begin by reducing each parcel to a single exemplar time series. This disconnect between parcel formation and usage assumes that parcel exemplars adequately represent their member voxels, which is not always the case for commonly used parcellations.

Uncovering hidden resting state dynamics: A new perspective on auditory verbal hallucinations

In the absence of sensory stimulation, the brain transits between distinct functional networks. Network dynamics such as transition patterns and the time the brain stays in each network link to cognition and behavior and are subject to much investigation. Auditory verbal hallucinations (AVH), the temporally fluctuating unprovoked experience of hearing voices, are associated with aberrant resting state network activity. However, we lack a clear understanding of how different networks contribute to aberrant activity over time. An accurate characterization of latent network dynamics and their relation to neurocognitive changes necessitates methods that capture the sub-second temporal fluctuations of the networks' functional connectivity signatures. Here, we critically evaluate the assumptions and sensitivity of several approaches commonly used to assess temporal dynamics of brain connectivity states in M/EEG and fMRI research, highlighting methodological constraints and their clinical relevance to AVH. Identifying altered brain connectivity states linked to AVH can facilitate the detection of predictive disease markers and ultimately be valuable for generating individual risk profiles, differential diagnosis, targeted intervention, and treatment strategies.

Abnormal phase-amplitude coupling characterizes the interictal state in epilepsy

OBJECTIVE

 Diagnosing epilepsy still requires visual interpretation of electroencephalography and magnetoencephalography (MEG) by specialists, which prevents quantification and standardization of diagnosis. Previous studies proposed automated diagnosis by combining various features from electroencephalography and MEG, such as relative power (Power) and functional connectivity. However, the usefulness of interictal phase-amplitude coupling (PAC) in diagnosing epilepsy is still unknown. We hypothesized that resting-state PAC would be different for patients with epilepsy in the interictal state and for healthy participants such that it would improve discrimination between the groups.

METHODS

 We obtained resting-state MEG and magnetic resonance imaging in 90 patients with epilepsy during their preoperative evaluation and in 90 healthy participants. We used the cortical currents estimated from MEG and magnetic resonance imaging to calculate Power in the δ (1-3 Hz), θ (4-7 Hz), α (8-13 Hz), β (13-30 Hz), low γ (35-55 Hz), and high γ (65-90 Hz) bands and functional connectivity in the θ band. PAC was evaluated using the synchronization index (SI) for eight frequency band pairs: the phases of δ, θ, α, and β and the amplitudes of low and high γ. First, we compared the mean SI values for the patients with epilepsy and the healthy participants. Then, using features such as PAC, Power, functional connectivity, and features extracted by deep learning individually or combined, we tested whether PAC improves discrimination accuracy for the two groups.

RESULTS

 The mean SI values were significantly different for the patients with epilepsy and the healthy participants. The SI value difference was highest for θ/low γ in the temporal lobe. Discrimination accuracy was the highest, at 90%, using the combination of PAC and deep learning.

SIGNIFICANCE

 Abnormal PAC characterized the patients with epilepsy in the interictal state compared with the healthy participants, potentially improving the discrimination of epilepsy.

Spectral and network characterization of focal seizure types and phases

BACKGROUND AND OBJECTIVE

Currently, epileptic seizure characterization relies on several clinical features that allow their classification into different types. The present work aims to characterize both seizure types and phases based exclusively on electrophysiological characteristics.

METHODS

Based on the analysis of intracranial EEG recordings of 129 seizures from 22 patients obtained from the European Epilepsy Database, network and spectral measures were calculated in five-second temporal windows. Statistically significant differences between each window of the seizure phases (preictal, ictal, and postictal) and the interictal phase were used to identify/classify seizure types and their phases. A support vector machine (SVM) working on a multidimensional feature space of network and spectral measures was implemented for the classification of each seizure type; a traditional statistical approach was also conducted to highlight the underlying patterns to each seizure type or phase.

RESULTS

The percentage of correct classification of seizure types, corrected by chance, provided by the SVM exceeded 70%, considering all measures and the entire seizure (preictal + ictal + postictal). This percentage increased to more than 80% when all the measures during the ictal period for the depth electrodes or during the postictal for subdural electrodes were considered. Regarding the statistical approach, several measures presented a monotonic ascending and descending behavior with respect to seizure severity; these changes were observed during the ictal and postictal periods. Some measures were specific of each seizure type.

CONCLUSIONS

Our results provide a new framework to seizure characterization and reveal the possibility of an exclusively intracranial EEG-based classification. This could be used to build an automatic seizure classification system and provides new evidence of the network-related physiopathology of epilepsies. Thus, the novelty of this work is the possibility of differentiating seizure types based exclusively on the EEG recordings, providing evidence of the underlying patterns or characteristics to each seizure type and/or phase that would allow their optimal classification.

Epidemic models characterize seizure propagation and the effects of epilepsy surgery in individualized brain networks based on MEG and invasive EEG recordings

Epilepsy surgery is the treatment of choice for drug-resistant epilepsy patients. However, seizure-freedom is currently achieved in only 2/3 of the patients after surgery. In this study we have developed an individualized computational model based on MEG brain networks to explore seizure propagation and the efficacy of different virtual resections. Eventually, the goal is to obtain individualized models to optimize resection strategy and outcome. We have modelled seizure propagation as an epidemic process using the susceptible-infected (SI) model on individual brain networks derived from presurgical MEG. We included 10 patients who had received epilepsy surgery and for whom the surgery outcome at least one year after surgery was known. The model parameters were tuned in in order to reproduce the patient-specific seizure propagation patterns as recorded with invasive EEG. We defined a personalized search algorithm that combined structural and dynamical information to find resections that maximally decreased seizure propagation for a given resection size. The optimal resection for each patient was defined as the smallest resection leading to at least a 90% reduction in seizure propagation. The individualized model reproduced the basic aspects of seizure propagation for 9 out of 10 patients when using the resection area as the origin of epidemic spreading, and for 10 out of 10 patients with an alternative definition of the seed region. We found that, for 7 patients, the optimal resection was smaller than the resection area, and for 4 patients we also found that a resection smaller than the resection area could lead to a 100% decrease in propagation. Moreover, for two cases these alternative resections included nodes outside the resection area. Epidemic spreading models fitted with patient specific data can capture the fundamental aspects of clinically observed seizure propagation, and can be used to test virtual resections in silico. Combined with optimization algorithms, smaller or alternative resection strategies, that are individually targeted for each patient, can be determined with the ultimate goal to improve surgery outcome. MEG-based networks can provide a good approximation of structural connectivity for computational models of seizure propagation, and facilitate their clinical use.

Infantile status epilepticus disrupts myelin development

Temporal lobe epilepsy (TLE) is the most prevalent type of epilepsy in adults; it often starts in infancy or early childhood. Although TLE is primarily considered to be a grey matter pathology, a growing body of evidence links this disease with white matter abnormalities. In this study, we explore the impact of TLE onset and progression in the immature brain on white matter integrity and development utilising the rat model of Li-pilocarpine-induced TLE at the 12th postnatal day (P). Diffusion tensor imaging (DTI) and Black-Gold II histology uncovered disruptions in major white matter tracks (corpus callosum, internal and external capsules, and deep cerebral white matter) spreading through the whole brain at P28. These abnormalities were mostly not present any longer at three months after TLE induction, with only limited abnormalities detectable in the external capsule and deep cerebral white matter. Relaxation Along a Fictitious Field in the rotating frame of rank 4 indicated that white matter changes observed at both timepoints, P28 and P72, are consistent with decreased myelin content. The animals affected by TLE-induced white matter abnormalities exhibited increased functional connectivity between the thalamus and medial prefrontal and somatosensory cortex in adulthood. Furthermore, histological analyses of additional animal groups at P15 and P18 showed only mild changes in white matter integrity, suggesting a gradual age-dependent impact of TLE progression. Taken together, TLE progression in the immature brain distorts white matter development with a peak around postnatal day 28, followed by substantial recovery in adulthood. This developmental delay might give rise to cognitive and behavioural comorbidities typical for early-onset TLE.

Increased resting state connectivity in the anterior default mode network of idiopathic epileptic dogs

Epilepsy is one of the most common chronic, neurological diseases in humans and dogs and considered to be a network disease. In human epilepsy altered functional connectivity in different large-scale networks have been identified with functional resting state magnetic resonance imaging. Since large-scale resting state networks have been consistently identified in anesthetised dogs’ application of this technique became promising in canine epilepsy research. The aim of the present study was to investigate differences in large-scale resting state networks in epileptic dogs compared to healthy controls. Our hypothesis was, that large-scale networks differ between epileptic dogs and healthy control dogs. A group of 17 dogs (Border Collies and Greater Swiss Mountain Dogs) with idiopathic epilepsy was compared to 20 healthy control dogs under a standardized sevoflurane anaesthesia protocol. Group level independent component analysis with dimensionality of 20 components, dual regression and two-sample t test were performed and revealed significantly increased functional connectivity in the anterior default mode network of idiopathic epileptic dogs compared to healthy control dogs (p = 0.00060). This group level differences between epileptic dogs and healthy control dogs identified using a rather simple data driven approach could serve as a starting point for more advanced resting state network analysis in epileptic dogs.

Stationary EEG pattern relates to large-scale resting state networks - An EEG-fMRI study connecting brain networks across time-scales

Relating brain dynamics acting on time scales that differ by at least an order of magnitude is a fundamental issue in brain research. The same is true for the observation of stable dynamical structures in otherwise highly non-stationary signals. The present study addresses both problems by the analysis of simultaneous resting state EEG-fMRI recordings of 53 patients with epilepsy. Confirming previous findings, we observe a generic and temporally stable average correlation pattern in EEG recordings. We design a predictor for the General Linear Model describing fluctuations around the stationary EEG correlation pattern and detect resting state networks in fMRI data. The acquired statistical maps are contrasted to several surrogate tests and compared with maps derived by spatial Independent Component Analysis of the fMRI data. By means of the proposed EEG-predictor we observe core nodes of known fMRI resting state networks with high specificity in the default mode, the executive control and the salience network. Our results suggest that both, the stationary EEG pattern as well as resting state fMRI networks are different expressions of the same brain activity. This activity is interpreted as the dynamics on (or close to) a stable attractor in phase space that is necessary to maintain the brain in an efficient operational mode. We discuss that this interpretation is congruent with the theoretical framework of complex systems as well as with the brain's energy balance.

Probabilistic mapping of thalamic nuclei and thalamocortical functional connectivity in idiopathic generalised epilepsy

It is well established that abnormal thalamocortical systems play an important role in the generation and maintenance of primary generalised seizures. However, it is currently unknown which thalamic nuclei and how nuclear‐specific thalamocortical functional connectivity are differentially impacted in patients with medically refractory and non‐refractory idiopathic generalised epilepsy (IGE). In the present study, we performed structural and resting‐state functional magnetic resonance imaging (MRI) in patients with refractory and non‐refractory IGE, segmented the thalamus into constituent nuclear regions using a probabilistic MRI segmentation method and determined thalamocortical functional connectivity using seed‐to‐voxel connectivity analyses. We report significant volume reduction of the left and right anterior thalamic nuclei only in patients with refractory IGE. Compared to healthy controls, patients with refractory and non‐refractory IGE had significant alterations of functional connectivity between the centromedian nucleus and cortex, but only patients with refractory IGE had altered cortical connectivity with the ventral lateral nuclear group. Patients with refractory IGE had significantly increased functional connectivity between the left and right ventral lateral posterior nuclei and cortical regions compared to patients with non‐refractory IGE. Cortical effects were predominantly located in the frontal lobe. Atrophy of the anterior thalamic nuclei and resting‐state functional hyperconnectivity between ventral lateral nuclei and cerebral cortex may be imaging markers of pharmacoresistance in patients with IGE. These structural and functional abnormalities fit well with the known importance of thalamocortical systems in the generation and maintenance of primary generalised seizures, and the increasing recognition of the importance of limbic pathways in IGE.

Relationship between Spatiotemporal Dynamics of the Brain at Rest and Self-Reported Spontaneous Thoughts: An EEG Microstate Approach

Rationale: The resting-state paradigm is frequently applied in electroencephalography (EEG) research; however, it is associated with the inability to control participants’ thoughts. To quantify subjects’ subjective experiences at rest, the Amsterdam Resting-State Questionnaire (ARSQ) was introduced covering ten dimensions of mind wandering. We aimed to estimate associations between subjective experiences and resting-state microstates of EEG. Methods: 5 min resting-state EEG data of 197 subjects was used to evaluate temporal properties of seven microstate classes. Bayesian correlation approach was implemented to assess associations between ARSQ domains assessed after resting and parameters of microstates. Results: Several associations between Comfort, Self and Somatic Awareness domains and temporal properties of neuroelectric microstates were revealed. The positive correlation between Comfort and duration of microstates E showed the strongest evidence (BF₁₀ > 10); remaining correlations showed substantial evidence (10 > BF₁₀ > 3). Conclusion: Our study indicates the relevance of assessments of spontaneous thought occurring during the resting-state for the understanding of the intrinsic brain activity reflected in microstates.

Prospective Quantitative Neuroimaging Analysis of Putative Temporal Lobe Epilepsy

Purpose: A prospective study of individual and combined quantitative imaging applications for lateralizing epileptogenicity was performed in a cohort of consecutive patients with a putative diagnosis of mesial temporal lobe epilepsy (mTLE). Methods: Quantitative metrics were applied to MRI and nuclear medicine imaging studies as part of a comprehensive presurgical investigation. The neuroimaging analytics were conducted remotely to remove bias. All quantitative lateralizing tools were trained using a separate dataset. Outcomes were determined after 2 years. Of those treated, some underwent resection, and others were implanted with a responsive neurostimulation (RNS) device. Results: Forty-eight consecutive cases underwent evaluation using nine attributes of individual or combinations of neuroimaging modalities: 1) hippocampal volume, 2) FLAIR signal, 3) PET profile, 4) multistructural analysis (MSA), 5) multimodal model analysis (MMM), 6) DTI uncertainty analysis, 7) DTI connectivity, and 9) fMRI connectivity. Of the 24 patients undergoing resection, MSA, MMM, and PET proved most effective in predicting an Engel class 1 outcome (>80% accuracy). Both hippocampal volume and FLAIR signal analysis showed 76% and 69% concordance with an Engel class 1 outcome, respectively. Conclusion: Quantitative multimodal neuroimaging in the context of a putative mTLE aids in declaring laterality. The degree to which there is disagreement among the various quantitative neuroimaging metrics will judge whether epileptogenicity can be confined sufficiently to a particular temporal lobe to warrant further study and choice of therapy. Prediction models will improve with continued exploration of combined optimal neuroimaging metrics.

Interictal epileptiform discharges changed epilepsy-related brain network architecture in BECTS

To investigate directed information flow of epileptiform activity in benign epilepsy with centrotemporal spikes (BECTS) during ictal epileptiform discharges (IEDs) and non-IEDs periods. In this multi-center study, a total of 188 subjects, including 50 BECTS and 138 normal children's controls (NCs) from three different centers (Center 1: females/males, 38/55; mean age, 9.33 ± 2.6 years; Center 2: females/males,7/10; mean age, 8.59 ± 2.32 years; Center 3: females/males, 14/14; mean age, 13 ± 3.42 years) were recruited. The BECTS were classified into IEDs (females/males, 12/15; mean age, 8.15 ± 1.68 years) and non-IEDs (females/males, 10/13; mean age, 9.09 ± 1.98 years) subgroups depending on presence of central-temporal spikes from an EEG-fMRI examination. Three new methods, structural equation parametric modeling, dynamic causal modeling and granger causality density (GCD) were used to determine optimal network architectures for BECTS. Three multicentric NCs determined a reliable and consistent network architecture by structural equation parametric modeling method. Further analyses were used for IEDs and non-IEDs to determine the brain network architecture by structural equation parametric modeling, dynamic causal modeling and GCD, respectively. The brain network architecture of IEDs substate, non-IEDs substate and NCs are different. IEDs promoted the driving effect of the Rolandic areas with more output information flows, and increased the targeted effect of the top of pre-/post-central gyrus with more input information flows. The information flow arises from the Rolandic areas, and subsequently propagates to the top of pre-/post-central gyrus and thalamus. From non-IEDs status to IEDs status, the thalamus load may play an important role in the modulation and regulation of epileptiform activity. These findings shed new light on pathophysiological mechanism of directed localization of epileptiform activity in BECTS.

Functional Connectivity of the Anterior Nucleus of the Thalamus in Pediatric Focal Epilepsy

Objective: Whilst stimulation of the anterior nucleus of the thalamus has shown efficacy for reducing seizure frequency in adults, alterations in thalamic connectivity have not been explored in children. We tested the hypotheses that (a) the anterior thalamus has increased functional connectivity in children with focal epilepsy, and (b) this alteration in the connectome is a persistent effect of the disease rather than due to transient epileptiform activity.

Methods: Data from 35 children (7–18 years) with focal, drug-resistant epilepsy and 20 healthy children (7–17 years) were analyzed. All subjects underwent functional magnetic resonance imaging (fMRI) whilst resting and were simultaneously monitored with scalp electroencephalography (EEG). The fMRI timeseries were extracted for each Automated Anatomical Labeling brain region and thalamic subregion. Graph theory metrics [degree (DC) and eigenvector (EC) centrality] were used to summarize the connectivity profile of the ipsilateral thalamus, and its thalamic parcellations. The effect of interictal epileptiform discharges (IEDs) captured on EEG was used to determine their effect on DC and EC.

Results: DC was significantly higher in the anterior nucleus (p = 0.04) of the thalamus ipsilateral to the epileptogenic zone in children with epilepsy compared to controls. On exploratory analyses, we similarly found a higher DC in the lateral dorsal nucleus (p = 0.02), but not any other thalamic subregion. No differences in EC measures were found between patients and controls. We did not find any significant difference in DC or EC in any thalamic subregion when comparing the results of children with epilepsy before, and after the removal of the effects of IEDs.

Conclusions: Our data suggest that the anterior and lateral dorsal nuclei of the thalamus are more highly functionally connected in children with poorly controlled focal epilepsy. We did not detect a convincing change in thalamic connectivity caused by transient epileptiform activity, suggesting that it represents a persistent alteration to network dynamics.

Hippocampal atrophy and quantitative EEG markers in mild cognitive impairment in temporal lobe epilepsy versus extra-temporal lobe epilepsy

OBJECTIVE

Cognitive impairment in temporal lobe epilepsy is widely acknowledged as one of the most well-known comorbidities. This study aimed to explore cognitive impairment and to determine the potential clinical, radiological, and quantitative electroencephalography markers for cognitive impairment in temporal lobe epilepsy patients versus extra-temporal lobe epilepsy.

METHODS

Forty-five patients with temporal lobe epilepsy and forty-five patients with extra-temporal lobe epilepsy were recruited for an administered digit span test, verbal fluency test, mini-mental state examination, digital symbol test, and Montreal cognitive assessment. Also, they were subjected to magnetic resonance imaging assessment for hippocampal atrophy and a quantitative electroencephalography assessment for electroencephalography markers (median frequency, peak frequency, and the alpha-to-theta ratio).

RESULTS

Patients with extra-temporal lobe epilepsy showed non-significant higher epilepsy durations and a higher frequency of seizures. Temporal lobe epilepsy patients showed a more statistically significant family history of epilepsy (37.7%), more history of febrile convulsions (13.3%), higher hippocampal atrophy (17.8%), and lower cognitive scales, especially mini-mental state examination and Montreal cognitive assessment; lower digital symbol test, verbal fluency test, and backward memory of digit span test. Also, temporal lobe epilepsy patients had a strong negative correlation with electroencephalography markers: median frequency, peak frequency, and the alpha-to-theta ratio (r =  - 0.68, P < 0.005 and r =  - 0.64, P < 0.005 and r =  - 0.66, P < 0.005 respectively).

CONCLUSION

Cognitive impairment in patients with temporal lobe epilepsy was correlated with hippocampal atrophy and quantitative electroencephalography abnormalities, especially peak frequency, median frequency, and alpha-to-theta ratio that could be used alone for the identification of early cognitive impairment.

TRIAL REGISTRATION

Clinicaltrials.gov: NCT04376671.

Reliability and availability of granger causality density in localization of Rolandic focus in BECTS

A new method, called granger causality density (GCD), could reflect the directed information flow of the epileptiform activity, which is much closely match with excitatory and inhibitory imbalance theory of epilepsy. Here, we investigated if GCD could effectively localize the Rolandic focus in 50 patients with benign childhood epilepsy with central-temporal spikes (BECTS) from 27 normal children. The BECTS were classified into ictal epileptiform discharges (IEDs; 12 females, 15 males;age, 8.15 ± 1.68 years) and non-IEDs (10 females, 13 males; age, 9.09 ± 1.98 years) subgroups depending on the presence of central-temporal spikes. Multiple correlation-modality analyses (Pearson, across-voxel and across-subject correlations) were used to calculate the couplings between the GCD maps and IEDs-related brain activation map. The individual lateralization coefficient of localize IEDs and multiple regression analysis were used to identify the reliability of the GCD method in localizing the Rolandic focus. In this study, multiple correlation-modality analyses showed that the IEDs-related brain activation map and the GCD maps had highly temporal (coefficient ׀r\= 0.56 ~ 0.65) and spatial (\r\=0.53~0.91) (r\=~ couplings. The proposed GCD method and multiple regression analyses showed consistent findings with the clinical EEG recordings in lateralization of Rolandic focus. Furthermore, the GCD method could reflect the epilepsy-related brain activity during non-IEDs substate. Therefore, the proposed GCD method has the potential to be served as an effective and reliable neuroimaging biomarker to localize the Rolandic focus of BECTS. These findings are critical for clinical early diagnosis, and may promote the progression of treatment and management of pediatric epilepsy.

Altered static and dynamic functional network connectivity in temporal lobe epilepsy with different disease duration and their relationships with attention

The brain network alterations associated with temporal lobe epilepsy (TLE) progression are still unclear. The purpose of this study was to investigate altered patterns of static and dynamic functional network connectivity (sFNC and dFNC) in TLE with different durations of disease. In this study, 19 TLE patients with a disease duration of ≤5 years (TLE-SD), 24 TLE patients with a disease duration of >5 years (TLE-LD), and 21 healthy controls (HCs) underwent resting-state functional magnetic resonance imaging and attention network test. We used group independent component analysis to determine the target resting-state networks. Sliding window correlation and k-means clustering analysis methods were used to obtain different dFNC states, temporal properties, and temporal variability. We then compared sFNC and dFNC between groups and found that compared with HCs, TLE-SD patients had increased sFNC between the dorsal attention network and sensorimotor network/visual network (VN), but decreased sFNC between the inferior-posterior default mode network and VN. In the strongly connected dFNC state, TLE-SD patients spent more time, had greater mean dwell time, and showed greater inconsistent abnormal network connectivity. There was a significant negative correlation between the temporal variability of auditory network- left fronto-parietal network connectivity and orienting effect. No significant differences in sFNC and dFNC were detected between TLE-LD and HC groups. These findings suggest that the damage and functional brain network abnormalities gradually occur in TLE patients after the onset of epilepsy, which might lead to functional network reorganization and compensatory remodeling as the disease progresses.

Localization of Epileptic Foci Based on Simultaneous EEG-fMRI Data

Combining functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) enables a non-invasive investigation of the human brain function and evaluation of the correlation of these two important modalities of brain activity. This paper explores recent reports on using advanced simultaneous EEG–fMRI methods proposed to map the regions and networks involved in focal epileptic seizure generation. One of the applications of EEG and fMRI combination as a valuable clinical approach is the pre-surgical evaluation of patients with epilepsy to map and localize the precise brain regions associated with epileptiform activity. In the process of conventional analysis using EEG–fMRI data, the interictal epileptiform discharges (IEDs) are visually extracted from the EEG data to be convolved as binary events with a predefined hemodynamic response function (HRF) to provide a model of epileptiform BOLD activity and use as a regressor for general linear model (GLM) analysis of the fMRI data. This review examines the methodologies involved in performing such studies, including techniques used for the recording of EEG inside the scanner, artifact removal, and statistical analysis of the fMRI signal. It then discusses the results reported for patients with primary generalized epilepsy and patients with different types of focal epileptic disorders. An important matter that these results have brought to light is that the brain regions affected by interictal epileptic discharges might not be limited to the ones where they have been generated. The developed methods can help reveal the regions involved in or affected by a seizure onset zone (SOZ). As confirmed by the reviewed literature, EEG–fMRI provides information that comes particularly useful when evaluating patients with refractory epilepsy for surgery.

Identifying Refractory Epilepsy Without Structural Abnormalities by Fusing the Common Spatial Patterns of Functional and Effective EEG Networks

Drug refractory epilepsy (RE) is believed to be associated with structural lesions, but some RE patients show no significant structural abnormalities (RE-no-SA) on conventional magnetic resonance imaging scans. Since most of the medically controlled epilepsy (MCE) patients also do not exhibit structural abnormalities, a reliable assessment needs to be developed to differentiate RE-no-SA patients and MCE patients to avoid misdiagnosis and inappropriate treatment. Using resting-state scalp electroencephalogram (EEG) datasets, we extracted the spatial pattern of network (SPN) features from the functional and effective EEG networks of both RE-no-SA patients and MCE patients. Compared to the performance of traditional resting-state EEG network properties, the SPN features exhibited remarkable superiority in classifying these two groups of epilepsy patients, and accuracy values of 90.00% and 80.00% were obtained for the SPN features of the functional and effective EEG networks, respectively. By further fusing the SPN features of functional and effective networks, we demonstrated that the highest accuracy value of 96.67% could be reached, with a sensitivity of 100% and specificity of 92.86%. Overall, these findings not only indicate that the fused functional and effective SPN features are promising as reliable measurements for distinguishing RE-no-SA patients and MCE patients but also may provide a new perspective to explore the complex neurophysiology of refractory epilepsy.

Retrosplenial Cortex Contributes to Network Changes during Seizures in the GAERS Absence Epilepsy Rat Model

Resting state-fMRI was performed to explore brain networks in Genetic Absence Epilepsy Rats from Strasbourg and in nonepileptic controls (NEC) during monitoring of the brain state by simultaneous optical Ca2+-recordings. Graph theoretical analysis allowed for the identification of acute and chronic network changes and revealed preserved small world topology before and after seizure onset. The most prominent acute change in network organization during seizures was the segregation of cortical regions from the remaining brain. Stronger connections between thalamic with limbic regions compared with preseizure state indicated network regularization during seizures. When comparing between strains, intrathalamic connections were prominent in NEC, on local level represented by higher thalamic strengths and hub scores. Subtle differences were observed for retrosplenial cortex (RS), forming more connections beyond cortex in epileptic rats, and showing a tendency to lateralization during seizures. A potential role of RS as hub between subcortical and cortical regions in epilepsy was supported by increased numbers of parvalbumin-positive (PV+) interneurons together with enhanced inhibitory synaptic activity and neuronal excitability in pyramidal neurons. By combining multimodal fMRI data, graph theoretical methods, and electrophysiological recordings, we identified the RS as promising target for modulation of seizure activity and/or comorbidities.

Shared and distinct global signal topography disturbances in subcortical and cortical networks in human epilepsy

Epilepsy is a common brain network disorder associated with disrupted large-scale excitatory and inhibitory neural interactions. Recent resting-state fMRI evidence indicates that global signal (GS) fluctuations that have commonly been ignored are linked to neural activity. However, the mechanisms underlying the altered global pattern of fMRI spontaneous fluctuations in epilepsy remain unclear. Here, we quantified GS topography using beta weights obtained from a multiple regression model in a large group of epilepsy with different subtypes (98 focal temporal epilepsy; 116 generalized epilepsy) and healthy population (n = 151). We revealed that the nonuniformly distributed GS topography across association and sensory areas in healthy controls was significantly shifted in patients. Particularly, such shifts of GS topography disturbances were more widespread and bilaterally distributed in the midbrain, cerebellum, visual cortex, and medial and orbital cortex in generalized epilepsy, whereas in focal temporal epilepsy, these networks spread beyond the temporal areas but mainly remain lateralized. Moreover, we found that these abnormal GS topography patterns were likely to evolve over the course of a longer epilepsy disease. Our study demonstrates that epileptic processes can potentially affect global excitation/inhibition balance and shift the normal GS topological distribution. These progressive topographical GS disturbances in subcortical-cortical networks may underlie pathophysiological mechanisms of global fluctuations in human epilepsy.

Regional abnormality of functional connectivity is associated with clinical manifestations in individuals with intractable focal epilepsy

We explored regional functional connectivity alterations in intractable focal epilepsy brains using resting-state functional MRI. Distributions of the network parameters (corresponding to degree and eigenvector centrality) measured at each brain region for all 25 patients were significantly different from age- and sex-matched control data that were estimated by a healthy control dataset (n = 582, 18-84 years old). The number of abnormal regions whose parameters exceeded the mean + 2 SD of age- and sex-matched data for each patient were associated with various clinical parameters such as the duration of illness and seizure severity. Furthermore, abnormal regions for each patient tended to have functional connections with each other (mean ± SD = 58.6 ± 20.2%), the magnitude of which was negatively related to the quality of life. The abnormal regions distributed within the default mode network with significantly higher probability (p < 0.05) in 7 of 25 patients. We consider that the detection of abnormal regions by functional connectivity analysis using a large number of control datasets is useful for the numerical assessment of each patient's clinical conditions, although further study is necessary to elucidate etiology-specific abnormalities.