Disruption, emergence and lateralization of brain network hubs in mesial temporal lobe epilepsy

Resting state connectivity biomarkers of seizure freedom after epilepsy surgery

Alterations in brain networks may cause the lowering of the seizure threshold and hypersynchronization that underlie the recurrence of unprovoked seizures in epilepsy. The aim of this work is to estimate functional network characteristics, which may help predicting outcome of epilepsy surgery. Twenty patients were studied (11 females, 9 males, mean age 33 years) with scalp-recorded HD-EEG in resting state (eyes closed, no interictal discharges) before intracranial evaluation, which allowed the precise determination of the epileptogenic zone. Dipole source time courses in the brain were estimated using Weighted Minimum Norm Estimate based on HD-EEG signals. Information inflow and outflow of atlas-based brain regions were computed using partial directed connectivity. A set of graph measures for pairwise connections in standard EEG frequency bands was calculated. After epilepsy surgery 10 patients were seizure-free (Engel 1a) and 10 patients continued suffering from seizures (Engel outcome worse than 1a). Inflow of the regions containing the epileptogenic zone in the beta and delta frequency bands was significantly lower in patients who achieved seizure-freedom after surgery, compared with patients who continued to have seizures (p = 0.012, and p = 0.026, respectively). Average path length in the beta frequency band was significantly higher in patients who achieved seizure freedom (p = 0.012). In the delta frequency band, local efficiency and clustering coefficient were significantly higher in patients who achieved seizure freedom (0.033, 0.046). In patients who achieved seizure freedom after surgery, the preoperative analysis of the epileptic network exhibited stronger separation of the region containing the seizure onset zone, with less inflow of information. In contrast, shorter paths within the epileptic network may facilitate hypersynchronous neuronal activity and thus the recurrence of seizures in non-seizure free patients. This study supports the hypothesis that epileptic network properties might help to define suitable candidates for epilepsy surgery.

Human brain state dynamics are highly reproducible and associated with neural and behavioral features

Neural activity and behavior vary within an individual (states) and between individuals (traits). However, the mapping of state-trait neural variation to behavior is not well understood. To address this gap, we quantify moment-to-moment changes in brain-wide co-activation patterns derived from resting-state functional magnetic resonance imaging. In healthy young adults, we identify reproducible spatiotemporal features of co-activation patterns at the single-subject level. We demonstrate that a joint analysis of state-trait neural variations and feature reduction reveal general motifs of individual differences, encompassing state-specific and general neural features that exhibit day-to-day variability. The principal neural variations co-vary with the principal variations of behavioral phenotypes, highlighting cognitive function, emotion regulation, alcohol and substance use. Person-specific probability of occupying a particular co-activation pattern is reproducible and associated with neural and behavioral features. This combined analysis of state-trait variations holds promise for developing reproducible neuroimaging markers of individual life functional outcome.

Characterizing white matter connectome abnormalities in patients with temporal lobe epilepsy using threshold-free network-based statistics

INTRODUCTION

Emerging evidence illustrates that temporal lobe epilepsy (TLE) involves network disruptions represented by hyperexcitability and other seizure-related neural plasticity. However, these associations are not well-characterized. Our study characterizes the whole brain white matter connectome abnormalities in TLE patients compared to healthy controls (HCs) from the prospective Epilepsy Connectome Project study. Furthermore, we assessed whether aberrant white matter connections are differentially related to cognitive impairment and a history of focal-to-bilateral tonic-clonic (FBTC) seizures.

METHODS

Multi-shell connectome MRI data were preprocessed using the DESIGNER guidelines. The IIT Destrieux gray matter atlas was used to derive the 162 × 162 structural connectivity matrices (SCMs) using MRTrix3. ComBat data harmonization was applied to harmonize the SCMs from pre- and post-scanner upgrade acquisitions. Threshold-free network-based statistics were used for statistical analysis of the harmonized SCMs. Cognitive impairment status and FBTC seizure status were then correlated with these findings.

RESULTS

We employed connectome measurements from 142 subjects, including 92 patients with TLE (36 males, mean age = 40.1 ± 11.7 years) and 50 HCs (25 males, mean age = 32.6 ± 10.2 years). Our analysis revealed overall significant decreases in cross-sectional area (CSA) of the white matter tract in TLE group compared to controls, indicating decreased white matter tract integrity and connectivity abnormalities in addition to apparent differences in graph theoretic measures of connectivity and network-based statistics. Focal and generalized cognitive impaired TLE patients showcased higher trend-level abnormalities in the white matter connectome via decreased CSA than those with no cognitive impairment. Patients with a positive FBTC seizure history also showed trend-level findings of association via decreased CSA.

CONCLUSIONS

Widespread global aberrant white matter connectome changes were observed in TLE patients and characterized by seizure history and cognitive impairment, laying a foundation for future studies to expand on and validate the novel biomarkers and further elucidate TLE's impact on brain plasticity.

NREM sleep brain networks modulate cognitive recovery from sleep deprivation

Decrease in cognitive performance after sleep deprivation followed by recovery after sleep suggests its key role, and especially non-rapid eye movement (NREM) sleep, in the maintenance of cognition. It remains unknown whether brain network reorganization in NREM sleep stages N2 and N3 can uniquely be mapped onto individual differences in cognitive performance after a recovery nap following sleep deprivation. Using resting state functional magnetic resonance imaging (fMRI), we quantified the integration and segregation of brain networks during NREM sleep stages N2 and N3 while participants took a 1-hour nap following 24-hour sleep deprivation, compared to well-rested wakefulness. Here, we advance a new analytic framework called the hierarchical segregation index (HSI) to quantify network segregation across spatial scales, from whole-brain to the voxel level, by identifying spatio-temporally overlapping large-scale networks and the corresponding voxel-to-region hierarchy. Our results show that network segregation increased in the default mode, dorsal attention and somatomotor networks during NREM sleep compared to wakefulness. Segregation within the visual, limbic, and executive control networks exhibited N2 versus N3 sleep-specific voxel-level patterns. More segregation during N3 was associated with worse recovery of working memory, executive attention, and psychomotor vigilance after the nap. The level of spatial resolution of network segregation varied among brain regions and was associated with the recovery of performance in distinct cognitive tasks. We demonstrated the sensitivity and reliability of voxel-level HSI to provide key insights into within-region variation, suggesting a mechanistic understanding of how NREM sleep replenishes cognition after sleep deprivation.

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.

Human brain state dynamics reflect individual neuro-phenotypes

Neural activity and behavior vary within an individual (states) and between individuals (traits). However, the mapping of state-trait neural variation to behavior is not well understood. To address this gap, we quantify moment-to-moment changes in brain-wide co-activation patterns derived from resting-state functional magnetic resonance imaging. In healthy young adults, we identify reproducible spatio-temporal features of co-activation patterns at the single subject level. We demonstrate that a joint analysis of state-trait neural variations and feature reduction reveal general motifs of individual differences, encompassing state-specific and general neural features that exhibit day-to-day variability. The principal neural variations co-vary with the principal variations of behavioral phenotypes, highlighting cognitive function, emotion regulation, alcohol and substance use. Person-specific probability of occupying a particular co-activation pattern is reproducible and associated with neural and behavioral features. This combined analysis of state-trait variations holds promise for developing reproducible neuroimaging markers of individual life functional outcome.

High-density electroencephalographic functional networks in genetic generalized epilepsy: Preserved whole-brain topology hides local reorganization

OBJECTIVE

Genetic generalized epilepsy (GGE) accounts for approximately 20% of adult epilepsy cases and is considered a disorder of large brain networks, involving both hemispheres. Most studies have not shown any difference in functional whole-brain network topology when compared to healthy controls. Our objective was to examine whether this preserved global network topology could hide local reorganizations that balance out at the global network level.

METHODS

We recorded high-density electroencephalograms from 20 patients and 20 controls, and reconstructed the activity of 118 regions. We computed functional connectivity in windows free of interictal epileptiform discharges in broad, delta, theta, alpha, and beta frequency bands, characterized the network topology, and used the Hub Disruption Index (HDI) to quantify the topological reorganization. We examined the generalizability of our results by reproducing a 25-electrode clinical system.

RESULTS

Our study did not reveal any significant change in whole-brain network topology among GGE patients. However, the HDI was significantly different between patients and controls in all frequency bands except alpha (p < .01, false discovery rate [FDR] corrected, d < -1), and accompanied by an increase in connectivity in the prefrontal regions and default mode network. This reorganization suggests that regions that are important in transferring the information in controls were less so in patients. Inversely, the crucial regions in patients are less so in controls. These findings were also found in delta and theta frequency bands when using 25 electrodes (p < .001, FDR corrected, d < -1).

SIGNIFICANCE

In GGE patients, the overall network topology is similar to that of healthy controls but presents a balanced local topological reorganization. This reorganization causes the prefrontal areas and default mode network to be more integrated and segregated, which may explain executive impairment associated with GGE. Additionally, the reorganization distinguishes patients from controls even when using 25 electrodes, suggesting its potential use as a diagnostic tool.

Hub overload and failure as a final common pathway in neurological brain network disorders

Understanding the concept of network hubs and their role in brain disease is now rapidly becoming important for clinical neurology. Hub nodes in brain networks are areas highly connected to the rest of the brain, which handle a large part of all the network traffic. They also show high levels of neural activity and metabolism, which makes them vulnerable to many different types of pathology. The present review examines recent evidence for the prevalence and nature of hub involvement in a variety of neurological disorders, emphasizing common themes across different types of pathology. In focal epilepsy, pathological hubs may play a role in spreading of seizure activity, and removal of such hub nodes is associated with improved outcome. In stroke, damage to hubs is associated with impaired cognitive recovery. Breakdown of optimal brain network organization in multiple sclerosis is accompanied by cognitive dysfunction. In Alzheimer's disease, hyperactive hub nodes are directly associated with amyloid-beta and tau pathology. Early and reliable detection of hub pathology and disturbed connectivity in Alzheimer's disease with imaging and neurophysiological techniques opens up opportunities to detect patients with a network hyperexcitability profile, who could benefit from treatment with anti-epileptic drugs.

Disentanglement of Resting State Brain Networks for Localizing Epileptogenic Zone in Focal Epilepsy

The objective of this study is to extract pathological brain networks from interictal period of E/MEG recordings to localize epileptic foci for presurgical evaluation. We proposed here a resting state E/MEG analysis framework, to disentangle brain functional networks represented by neural oscillations. By using an Embedded Hidden Markov Model, we constructed a state space for resting state recordings consisting of brain states with different spatiotemporal patterns. Functional connectivity analysis along with graph theory was applied on the extracted brain states to quantify the network features of the extracted brain states, based on which the source location of pathological states is determined. The method is evaluated by computer simulations and our simulation results revealed the proposed framework can extract brain states with high accuracy regarding both spatial and temporal profiles. We further evaluated the framework as compared with intracranial EEG defined seizure onset zone in 10 patients with drug-resistant focal epilepsy who underwent MEG recordings and were seizure free after surgical resection. The real patient data analysis showed very good localization results using the extracted pathological brain states in 6/10 patients, with localization error of about 15 mm as compared to the seizure onset zone. We show that the pathological brain networks can be disentangled from the resting-state electromagnetic recording and could be identified based on the connectivity features. The framework can serve as a useful tool in extracting brain functional networks from noninvasive resting state electromagnetic recordings, and promises to offer an alternative to aid presurgical evaluation guiding intracranial EEG electrodes implantation.

Estimation of fMRI responses related to epileptic discharges using Bayesian hierarchical modeling

Simultaneous electroencephalography-functional MRI (EEG-fMRI) is a unique and noninvasive method for epilepsy presurgical evaluation. When selecting voxels by null-hypothesis tests, the conventional analysis may overestimate fMRI response amplitudes related to interictal epileptic discharges (IEDs), especially when IEDs are rare. We aimed to estimate fMRI response amplitudes represented by blood oxygen level dependent (BOLD) percentage changes related to IEDs using a hierarchical model. It involves the local and distributed hemodynamic response homogeneity to regularize estimations. Bayesian inference was applied to fit the model. Eighty-two epilepsy patients who underwent EEG-fMRI and subsequent surgery were included in this study. A conventional voxel-wise general linear model was compared to the hierarchical model on estimated fMRI response amplitudes and on the concordance between the highest response cluster and the surgical cavity. The voxel-wise model overestimated fMRI responses compared to the hierarchical model, evidenced by a practically and statistically significant difference between the estimated BOLD percentage changes. Only the hierarchical model differentiated brief and long-lasting IEDs with significantly different BOLD percentage changes. Overall, the hierarchical model outperformed the voxel-wise model on presurgical evaluation, measured by higher prediction performance. When compared with a previous study, the hierarchical model showed higher performance metric values, but the same or lower sensitivity. Our results demonstrated the capability of the hierarchical model of providing more physiologically reasonable and more accurate estimations of fMRI response amplitudes induced by IEDs. To enhance the sensitivity of EEG-fMRI for presurgical evaluation, it may be necessary to incorporate more appropriate spatial priors and bespoke decision strategies.

Structural brain network analysis in occipital lobe epilepsy

BACKGROUND

This study aimed to analyze the structural brain network in patients with occipital lobe epilepsy (OLE) and investigate the differences in structural brain networks between patients with OLE and healthy controls.

METHODS

Patients with OLE and healthy controls with normal brain MRI findings were enrolled. They underwent diffusion tensor imaging using a 3.0T MRI scanner, and we computed the network measures of global and local structural networks in patients with OLE and healthy controls using the DSI studio program. We compared network measures between the groups.

RESULTS

We enrolled 23 patients with OLE and 42 healthy controls. There were significant differences in the global structural network between patients with OLE and healthy controls. The assortativity coefficient (-0.0864 vs. -0.0814, p = 0.0214), mean clustering coefficient (0.0061 vs. 0.0064, p = 0.0203), global efficiency (0.0315 vs. 0.0353, p = 0.0086), and small-worldness index (0.0001 vs. 0.0001, p = 0.0175) were lower, whereas the characteristic path length (59.2724 vs. 53.4684, p = 0.0120) was higher in patients with OLE than those in the healthy controls. There were several nodes beyond the occipital lobe that showed significant differences in the local structural network between the groups. In addition, the assortativity coefficient was negatively correlated with the duration of epilepsy (r=-0.676, p = 0.001).

Improved Seizure Onset-Zone Lateralization in Temporal Lobe Epilepsy using 7T Resting-State fMRI: A Direct Comparison with 3T

Objective

Resting-state functional magnetic resonance imaging (rs-fMRI) at ultra high-field strengths (≥7T) is known to provide superior signal-to-noise and statistical power than comparable acquisitions at lower field strengths. In this study, we aim to provide a direct comparison of the seizure onset-zone (SOZ) lateralizing ability of 7T rs-fMRI and 3T rs-fMRI.

Methods

We investigated a cohort of 70 temporal lobe epilepsy (TLE) patients. A paired cohort of 19 patients had 3T and 7T rs-fMRI acquisitions for direct comparison between the two field strengths. Forty-three patients had only 3T, and 8 patients had only 7T rs-fMRI acquisitions. We quantified the functional connectivity between the hippocampus and other nodes within the default mode network (DMN) using seed-to-voxel connectivity, and measured how hippocampo-DMN connectivity could inform SOZ lateralization at 7T and 3T field strengths.

Results

Differences between hippocampo-DMN connectivity ipsilateral and contralateral to the SOZ were significantly higher at 7T (pFDR=0.008) than at 3T (pFDR=0.80) when measured in the same subjects. We found that our ability to lateralize the SOZ, by distinguishing subjects with left TLE from subjects with right TLE, was superior at 7T (AUC = 0.97) than 3T (AUC = 0.68). Our findings were reproduced in extended cohorts of subjects scanned at either 3T or 7T. Our rs-fMRI findings at 7T, but not 3T, are consistent and highly correlated (Spearman Rho=0.65) with clinical FDG-PET lateralizing hypometabolism.

Significance

We show superior SOZ lateralization in TLE patients when using 7T relative to 3T rs-fMRI, supporting the adoption of high-field strength functional imaging in the epilepsy presurgical evaluation.

Neurophysiology, Neuropsychology, and Epilepsy, in 2022: Hills We Have Climbed and Hills Ahead. Neurophysiology in epilepsy

Since the discovery of the human electroencephalogram (EEG), neurophysiology techniques have become indispensable tools in our armamentarium to localize epileptic seizures. New signal analysis techniques and the prospects of artificial intelligence and big data will offer unprecedented opportunities to further advance the field in the near future, ultimately resulting in improved quality of life for many patients with drug-resistant epilepsy. This article summarizes selected presentations from Day 1 of the two-day symposium "Neurophysiology, Neuropsychology, Epilepsy, 2022: Hills We Have Climbed and the Hills Ahead". Day 1 was dedicated to highlighting and honoring the work of Dr. Jean Gotman, a pioneer in EEG, intracranial EEG, simultaneous EEG/ functional magnetic resonance imaging, and signal analysis of epilepsy. The program focused on two main research directions of Dr. Gotman, and was dedicated to "High-frequency oscillations, a new biomarker of epilepsy" and "Probing the epileptic focus from inside and outside". All talks were presented by colleagues and former trainees of Dr. Gotman. The extended summaries provide an overview of historical and current work in the neurophysiology of epilepsy with emphasis on novel EEG biomarkers of epilepsy and source imaging and concluded with an outlook on the future of epilepsy research, and what is needed to bring the field to the next level.

Arousal and salience network connectivity alterations in surgical temporal lobe epilepsy

OBJECTIVE

It is poorly understood why patients with mesial temporal lobe epilepsy (TLE) have cognitive deficits and brain network changes that extend beyond the temporal lobe, including altered extratemporal intrinsic connectivity networks (ICNs). However, subcortical arousal structures project broadly to the neocortex, are affected by TLE, and thus may contribute to these widespread network effects. The authors' objective was to examine functional connectivity (FC) patterns between subcortical arousal structures and neocortical ICNs, possible neurocognitive relationships, and FC changes after epilepsy surgery.

METHODS

The authors obtained resting-state functional magnetic resonance imaging (fMRI) in 50 adults with TLE and 50 controls. They compared nondirected FC (correlation) and directed FC (Granger causality laterality index) within the salience network, default mode network, and central executive network, as well as between subcortical arousal structures; these 3 ICNs were also compared between patients and controls. They also used an fMRI-based vigilance index to relate alertness to arousal center FC. Finally, fMRI was repeated in 29 patients > 12 months after temporal lobe resection.

RESULTS

Nondirected FC within the salience (p = 0.042) and default mode (p = 0.0008) networks, but not the central executive network (p = 0.79), was decreased in patients in comparison with controls (t-tests, corrected). Nondirected FC between the salience network and subcortical arousal structures (nucleus basalis of Meynert, thalamic centromedian nucleus, and brainstem pedunculopontine nucleus) was reduced in patients in comparison with controls (p = 0.0028-0.015, t-tests, corrected), and some of these connectivity abnormalities were associated with lower processing speed index, verbal comprehension, and full-scale IQ. Interestingly, directed connectivity measures suggested a loss of top-down influence from the salience network to the arousal nuclei in patients. After resection, certain FC patterns between the arousal nuclei and salience network moved toward control values in the patients, suggesting that some postoperative recovery may be possible. Although an fMRI-based vigilance measure suggested that patients exhibited reduced alertness over time, FC abnormalities between the salience network and arousal structures were not influenced by the alertness levels during the scans.

CONCLUSIONS

FC abnormalities between subcortical arousal structures and ICNs, such as the salience network, may be related to certain neurocognitive deficits in TLE patients. Although TLE patients demonstrated vigilance abnormalities, baseline FC perturbations between the arousal and salience networks are unlikely to be driven solely by alertness level, and some may improve after surgery. Examination of the arousal network and ICN disturbances may improve our understanding of the downstream clinical effects of TLE.

Influence of the Peripheral Nervous System on Murine Osteoporotic Fracture Healing and Fracture-Induced Hyperalgesia

Osteoporotic fractures are often linked to persisting chronic pain and poor healing outcomes. Substance P (SP), α-calcitonin gene-related peptide (α-CGRP) and sympathetic neurotransmitters are involved in bone remodeling after trauma and nociceptive processes, e.g., fracture-induced hyperalgesia. We aimed to link sensory and sympathetic signaling to fracture healing and fracture-induced hyperalgesia under osteoporotic conditions. Externally stabilized femoral fractures were set 28 days after OVX in wild type (WT), α-CGRP- deficient (α-CGRP -/-), SP-deficient (Tac1-/-) and sympathectomized (SYX) mice. Functional MRI (fMRI) was performed two days before and five and 21 days post fracture, followed by µCT and biomechanical tests. Sympathectomy affected structural bone properties in the fracture callus whereas loss of sensory neurotransmitters affected trabecular structures in contralateral, non-fractured bones. Biomechanical properties were mostly similar in all groups. Both nociceptive and resting-state (RS) fMRI revealed significant baseline differences in functional connectivity (FC) between WT and neurotransmitter-deficient mice. The fracture-induced hyperalgesia modulated central nociception and had robust impact on RS FC in all groups. The changes demonstrated in RS FC in fMRI might potentially be used as a bone traumata-induced biomarker regarding fracture healing under pathophysiological musculoskeletal conditions. The findings are of clinical importance and relevance as they advance our understanding of pain during osteoporotic fracture healing and provide a potential imaging biomarker for fracture-related hyperalgesia and its temporal development. Overall, this may help to reduce the development of chronic pain after fracture thereby improving the treatment of osteoporotic fractures.

Functional connectivity between mesial temporal and default mode structures may help lateralize surgical temporal lobe epilepsy

OBJECTIVE

The most common surgically treatable epilepsy syndrome is mesial temporal lobe epilepsy (mTLE). Preoperative noninvasive lateralization of mTLE is challenging in part due to rapid contralateral seizure spread. Abnormal connections in both the mesial temporal lobe and resting-state networks have been described in mTLE, but it is unclear if connectivity between these networks may aid in lateralization.

METHODS

In 52 patients with left mTLE (LmTLE) or right mTLE (RmTLE) and 52 matched control subjects, the authors acquired 20 minutes of resting-state functional MRI (fMRI) and evaluated functional connectivity of bilateral hippocampi and amygdalae with selected resting-state networks. They used Pearson correlation, network-based statistic, and dynamic causal modeling. Also, to evaluate the clinical utility of a resting-state connectivity model in lateralizing unilateral presurgical mTLE patients, they used receiver operating characteristic curve analysis.

RESULTS

RmTLE patients demonstrated decreased nondirected connectivity between the right hippocampus and default mode network compared with LmTLE patients and control subjects. Network-based statistic analysis revealed that the network with most decreased connectivity that distinguished LmTLE from RmTLE patients included the right hippocampus and amygdala, right lateral orbitofrontal cortices, and bilateral inferior parietal lobules, precuneus, and medial orbitofrontal cortices. Dynamic causal modeling analysis revealed that cross-hemispheric connectivity between hippocampi and amygdalae was predominantly inward toward the epileptogenic side. A regression model incorporating these connectivity patterns was used to accurately lateralize mTLE patients with an area under the receiver operating characteristic curve of 0.87.

CONCLUSIONS

Evaluating fMRI connectivity between mesial temporal structures and default mode network may aid in mTLE lateralization, reduce need for intracranial monitoring, and guide surgical planning.

Arousal impacts distributed hubs modulating the integration of brain functional connectivity

Even when subjects are at rest, it is thought that brain activity is organized into distinct brain states during which reproducible patterns are observable. Yet, it is unclear how to define or distinguish different brain states. A potential source of brain state variation is arousal, which may play a role in modulating functional interactions between brain regions. Here, we use simultaneous resting state functional magnetic resonance imaging (fMRI) and pupillometry to study the impact of arousal levels indexed by pupil area on the integration of large-scale brain networks. We employ a novel sparse dictionary learning-based method to identify hub regions participating in between-network integration stratified by arousal, by measuring k-hubness, the number (k) of functionally overlapping networks in each brain region. We show evidence of a brain-wide decrease in between-network integration and inter-subject variability at low relative to high arousal, with differences emerging across regions of the frontoparietal, default mode, motor, limbic, and cerebellum networks. State-dependent changes in k-hubness relate to the actual patterns of network integration within these hubs, suggesting a brain state transition from high to low arousal characterized by global synchronization and reduced network overlaps. We demonstrate that arousal is not limited to specific brain areas known to be directly associated with arousal regulation, but instead has a brain-wide impact that involves high-level between-network communications. Lastly, we show a systematic change in pairwise fMRI signal correlation structures in the arousal state-stratified data, and demonstrate that the choice of global signal regression could result in different conclusions in conventional graph theoretical analysis and in the analysis of k-hubness when studying arousal modulations. Together, our results suggest the presence of global and local effects of pupil-linked arousal modulations on resting state brain functional connectivity.

The Social Connectome - Moving Toward Complexity in the Study of Brain Networks and Their Interactions in Social Cognitive and Affective Neuroscience

Over the past 150 years of neuroscientific research, the field has undergone a tremendous evolution. Starting out with lesion-based inference of brain function, functional neuroimaging, introduced in the late 1980s, and increasingly fine-grained and sophisticated methods and analyses now allow us to study the live neural correlates of complex behaviors in individuals and multiple agents simultaneously. Classically, brain-behavior coupling has been studied as an association of a specific area in the brain and a certain behavioral outcome. This has been a crucial first step in understanding brain organization. Social cognitive processes, as well as their neural correlates, have typically been regarded and studied as isolated functions and blobs of neural activation. However, as our understanding of the social brain as an inherently dynamic organ grows, research in the field of social neuroscience is slowly undergoing the necessary evolution from studying individual elements to how these elements interact and their embedding within the overall brain architecture. In this article, we review recent studies that investigate the neural representation of social cognition as interacting, complex, and flexible networks. We discuss studies that identify individual brain networks associated with social affect and cognition, interaction of these networks, and their relevance for disorders of social affect and cognition. This perspective on social cognitive neuroscience can highlight how a more fine-grained understanding of complex network (re-)configurations could improve our understanding of social cognitive deficits in mental disorders such as autism spectrum disorder and schizophrenia, thereby providing new impulses for methods of interventions.

A study of brain functional network and alertness changes in temporal lobe epilepsy with and without focal to bilateral tonic-clonic seizures

BACKGROUND

Temporal lobe epilepsy (TLE) is commonly refractory. Epilepsy surgery is an effective treatment strategy for refractory epilepsy, but patients with a history of focal to bilateral tonic-clonic seizures (FBTCS) have poor outcomes. Previous network studies on epilepsy have found that TLE and idiopathic generalized epilepsy with generalized tonic-clonic seizures (IGE-GTCS) showed altered global and nodal topological properties. Alertness deficits also were found in TLE. However, FBTCS is a common type of seizure in TLE, and the implications for alertness as well as the topological rearrangements associated with this seizure type are not well understood.

METHODS

We obtained rs-fMRI data and collected the neuropsychological assessment data from 21 TLE patients with FBTCS (TLE- FBTCS), 18 TLE patients without FBTCS (TLE-non- FBTCS) and 22 controls, and constructed their respective functional brain networks. The topological properties were analyzed using the graph theoretical approach and correlations between altered topological properties and alertness were analyzed.

RESULTS

We found that TLE-FBTCS patients showed more serious impairment in alertness effect, intrinsic alertness and phasic alertness than the patients with TLE-non-FBTCS. They also showed significantly higher small-worldness, normalized clustering coefficient (γ) and a trend of higher global network efficiency (gE) compared to TLE-non-FBTCS patients. The gE showed a significant negative correlation with intrinsic alertness for TLE-non-FBTCS patients.

CONCLUSION

Our findings show different impairments in brain network information integration, segregation and alertness between the patients with TLE-FBTCS and TLE-non-FBTCS, demonstrating that impairments of the brain network may underlie the disruptions in alertness functions.

Functional connectivity of hippocampus in temporal lobe epilepsy depends on hippocampal dominance: a systematic review of the literature

BACKGROUND

Lateralized alterations in hippocampal function in the resting-state have been demonstrated for patients with temporal lobe epilepsy (TLE). However, resting-state fMRI of the hippocampus has yet to be substantiated as an adjunct to standard pre-operative assessments of the seizure focus.

OBJECTIVE

Here we report the results of a systematic review of resting-state fMRI studies investigating laterality of hippocampal network connectivity in TLE patients.

METHODS

A search of the PubMed, SCOPUS, Web of Science, and Embase databases for full-length articles written in English was conducted through June 2020 using the following terms: 'resting state fMRI,' 'hippocampus,' 'epilepsy,' and 'laterality.'

RESULTS

Our literature search yielded a total of 42 papers. After excluding studies that did not include patients with epilepsy, utilize resting-state fMRI, or explore the relationship between functional connectivity and disease lateralization, 20 publications were selected for inclusion. From these studies, a total of 528 patients, 258 with left TLE and 270 with right TLE, and 447 healthy controls were included. Of the 20 studies included, 18 found that patients with TLE demonstrated decreased hippocampal functional connectivity ipsilateral to the epileptogenic focus and 10 additionally reported increased hippocampal functional connectivity contralateral to the epileptogenic focus. Several studies demonstrated that the duration of disease was correlated with these changes in functional connectivity. This implies that a compensatory mechanism may be present in patients with treatment-refractory TLE.

CONCLUSION

The consistency of this hippocampal connectivity pattern across multiple studies suggests resting-state fMRI may be useful as a non-invasive diagnostic tool for preoperative evaluation of TLE patients.

Decreased Intrinsic Neural Timescales in Mesial Temporal Lobe Epilepsy

It is well established that epilepsy is characterized by the destruction of the information capacity of brain network and the interference with information processing in regions outside the epileptogenic focus. However, the potential mechanism remains poorly understood. In the current study, we applied a recently proposed approach on the basis of resting-state fMRI data to measure altered local neural dynamics in mesial temporal lobe epilepsy (mTLE), which represents how long neural information is stored in a local brain area and reflect an ability of information integration. Using resting-state-fMRI data recorded from 36 subjects with mTLE and 36 healthy controls, we calculated the intrinsic neural timescales (INT) of neural signals by summing the positive magnitude of the autocorrelation of the resting-state brain activity. Compared to healthy controls, the INT values were significantly lower in patients in the right orbitofrontal cortices, right insula, and right posterior lobe of cerebellum. Whereas, we observed no statistically significant changes between patients with long- and short-term epilepsy duration or between left-mTLE and right-mTLE. Our study provides distinct insight into the brain abnormalities of mTLE from the perspective of the dynamics of the brain activity, highlighting the significant role of intrinsic timescale in understanding neurophysiological mechanisms. And we postulate that altered intrinsic timescales of neural signals in specific cortical brain areas may be the neurodynamic basis of cognitive impairment and emotional comorbidities in mTLE patients.

Functional network topology in drug resistant and well-controlled idiopathic generalized epilepsy: a resting state functional MRI study

Despite an increasing number of drug treatment options for people with idiopathic generalized epilepsy (IGE), drug resistance remains a significant issue and the mechanisms underlying it remain poorly understood. Previous studies have largely focused on potential cellular or genetic explanations for drug resistance. However, epilepsy is understood to be a network disorder and there is a growing body of literature suggesting altered topology of large-scale resting networks in people with epilepsy compared with controls. We hypothesize that network alterations may also play a role in seizure control. The aim of this study was to compare resting state functional network structure between well-controlled IGE (WC-IGE), drug resistant IGE (DR-IGE) and healthy controls. Thirty-three participants with IGE (10 with WC-IGE and 23 with DR-IGE) and 34 controls were included. Resting state functional MRI networks were constructed using the Functional Connectivity Toolbox (CONN). Global graph theoretic network measures of average node strength (an equivalent measure to mean degree in a network that is fully connected), node strength distribution variance, characteristic path length, average clustering coefficient, small-world index and average betweenness centrality were computed. Graphs were constructed separately for positively weighted connections and for absolute values. Individual nodal values of strength and betweenness centrality were also measured and ‘hub nodes’ were compared between groups. Outcome measures were assessed across the three groups and between both groups with IGE and controls. The IGE group as a whole had a higher average node strength, characteristic path length and average betweenness centrality. There were no clear differences between groups according to seizure control. Outcome metrics were sensitive to whether negatively correlated connections were included in network construction. There were no clear differences in the location of ‘hub nodes’ between groups. The results suggest that, irrespective of seizure control, IGE interictal network topology is more regular and has a higher global connectivity compared to controls, with no alteration in hub node locations. These alterations may produce a resting state network that is more vulnerable to transitioning to the seizure state. It is possible that the lack of apparent influence of seizure control on network topology is limited by challenges in classifying drug response. It is also demonstrated that network topological features are influenced by the sign of connectivity weights and therefore future methodological work is warranted to account for anticorrelations in graph theoretic studies.

How cerebral cortex protects itself from interictal spikes: The alpha/beta inhibition mechanism

Interactions between interictal epileptiform discharges (IEDs) and distant cortical regions subserve potential effects on cognition of patients with focal epilepsy. We hypothesize that "healthy" brain areas at a distance from the epileptic focus may respond to the interference of IEDs by generating inhibitory alpha and beta oscillations. We predict that more prominent alpha-beta oscillations can be found in patients with less impaired neurocognitive profile. We performed a source imaging magnetoencephalography study, including 41 focal epilepsy patients: 21 with frontal lobe epilepsy (FLE) and 20 with mesial temporal lobe epilepsy. We investigated the effect of anterior (i.e., frontal and temporal) IEDs on the oscillatory pattern over posterior head regions. We compared cortical oscillations (5-80 Hz) temporally linked to 3,749 IEDs (1,945 frontal and 1,803 temporal) versus an equal number of IED-free segments. We correlated results from IED triggered oscillations to global neurocognitive performance. Only frontal IEDs triggered alpha-beta oscillations over posterior head regions. IEDs with higher amplitude triggered alpha-beta oscillations of higher magnitude. The intensity of posterior head region alpha-beta oscillations significantly correlated with a better neuropsychological profile. Our study demonstrated that cerebral cortex protects itself from IEDs with generation of inhibitory alpha-beta oscillations at distant cortical regions. The association of more prominent oscillations with a better cognitive status suggests that this mechanism might play a role in determining the cognitive resilience in patients with FLE.

Inferring the heritability of large-scale functional networks with a multivariate ACE modeling approach

Recent evidence suggests that the human functional connectome is stable at different timescales and is unique. These characteristics posit the functional connectome not only as an individual marker but also as a powerful discriminatory measure characterized by high intersubject variability. Among distinct sources of intersubject variability, the long-term sources include functional patterns that emerge from genetic factors. Here, we sought to investigate the contribution of additive genetic factors to the variability of functional networks by determining the heritability of the connectivity strength in a multivariate fashion. First, we reproduced and extended the connectome fingerprinting analysis to the identification of twin pairs. Then, we estimated the heritability of functional networks by a multivariate ACE modeling approach with bootstrapping. Twin pairs were identified above chance level using connectome fingerprinting, with monozygotic twin identification accuracy equal to 57.2% on average for whole-brain connectome. Additionally, we found that a visual (0.37), the medial frontal (0.31), and the motor (0.30) functional networks were the most influenced by additive genetic factors. Our findings suggest that genetic factors not only partially determine intersubject variability of the functional connectome, such that twins can be identified using connectome fingerprinting, but also differentially influence connectivity strength in large-scale functional networks.

The functional connectome is a unique representation of the functional organization of the human brain. As such, it has been extensively used as an individual marker, a “fingerprint,” because of its high intersubject variability. Here, we sought to investigate the influence of genetic factors on intersubject variability of functional networks. Therefore, we extended the connectome fingerprinting analysis to the identification of twin pairs, and we estimated the heritability of functional networks by a multivariate ACE modeling approach with bootstrapping. We found that genetic factors not only partially determine intersubject variability of the functional connectome, such that monozygotic twin identification accuracy achieved 57.2% on average using whole-brain connectome in the fingerprinting analysis, but also differentially influence connectivity strength in large-scale functional networks.

Reconfiguration dynamics of a language-and-memory network in healthy participants and patients with temporal lobe epilepsy

Current theoretical frameworks suggest that human behaviors are based on strong and complex interactions between cognitive processes such as those underlying language and memory functions in normal and neurological populations. We were interested in assessing the dynamic cerebral substrate of such interaction between language and declarative memory, as the composite function, in healthy controls (HC, N = 19) and patients with temporal lobe epilepsy (TLE, N = 16). Our assumption was that the language and declarative memory integration is based on a language-and-memory network (LMN) that is dynamic and reconfigures according to task demands and brain status. Therefore, we explored two types of LMN dynamics, a state reconfiguration (intrinsic resting-state compared to extrinsic state assessed with a sentence recall task) and a reorganization of state reconfiguration (TLE compared to HC). The dynamics was evaluated in terms of segregation (community or module detection) and integration (connector hubs). In HC, the level of segregation was the same in both states and the mechanism of LMN state reconfiguration was shown through module change of key language and declarative memory regions with integrative roles. In TLE patients, the reorganization of LMN state reconfiguration was reflected in segregation increase and extrinsic modules that were based on shorter-distance connections. While lateral and mesial temporal regions enabled state reconfiguration in HC, these regions showed reduced flexibility in TLE. We discuss our results in a connectomic perspective and propose a dynamic model of language and declarative memory functioning. We claim that complex and interactive cognitive functions, such as language and declarative memory, should be investigated dynamically, considering the interaction between cognitive networks.

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.

Depressive and Anxiety Symptoms Are Predictors of Seizure Recurrence in Adults With Newly Diagnosed Epilepsy

Objective: To investigate whether emerging depressive and anxiety symptoms are predictors of seizure recurrence in a cohort of patients with newly diagnosed epilepsy (PWNDE) who did not have a history of psychiatric diagnosis. Methods: A cohort of 283 PWNDE were psychiatrically assessed before antiseizure medication (ASM) therapy and were followed for 12 months to assess seizure recurrence. The influence of depressive and anxiety symptoms score on seizure recurrence was assessed using univariate and multivariate binary logistic regression analysis. Receiver operating characteristic (ROC) curve analysis was utilized. Results: A total of 283 individuals were included in final analysis, and 115 patients (40.6%) experienced seizure recurrence during follow-up. In multivariate logistic regression analysis, NDDI-E and GAD-7 score were associated with an increased risk of seizure recurrence with an adjusted OR of 1.360 (CI: 1.176-1.572; P < 0.001) and 1.101 (CI: 1.004-1.209; P = 0.041), respectively. Additionally, the adjusted OR and 95% CI of seizure recurrence for the "high NDDI-E score and high GAD-7 score" vs. "not high NDDI-E score and not high GAD-7 score" was 7.059 (3.521-14.149) (P for trend < 0.001). Conclusion: We found that an emergence of new psychiatric symptoms including depressive and anxiety symptoms were predictors of seizure recurrence in adults with newly diagnosed epilepsy who did not have psychiatric history.

Cognitive function: holarchy or holacracy?

Cognition is the most complex function of the brain. When exploring the inner workings of cognitive processes, it is crucial to understand the complexity of the brain's dynamics. This paper aims to describe the integrated framework of the cognitive function, seen as the result of organization and interactions between several systems and subsystems. We briefly describe several organizational concepts, spanning from the reductionist hierarchical approach, up to the more dynamic theory of open complex systems. The homeostatic regulation of the mechanisms responsible for cognitive processes is showcased as a dynamic interplay between several anticorrelated mechanisms, which can be found at every level of the brain's organization, from molecular and cellular level to large-scale networks (e.g., excitation-inhibition, long-term plasticity-long-term depression, synchronization-desynchronization, segregation-integration, order-chaos). We support the hypothesis that cognitive function is the consequence of multiple network interactions, integrating intricate relationships between several systems, in addition to neural circuits.

Identification of neural networks preferentially engaged by epileptogenic mass lesions through lesion network mapping analysis

Lesion network mapping (LNM) has been applied to true lesions (e.g., cerebrovascular lesions in stroke) to identify functionally connected brain networks. No previous studies have utilized LNM for analysis of intra-axial mass lesions. Here, we implemented LNM for identification of potentially vulnerable epileptogenic networks in mass lesions causing medically-refractory epilepsy (MRE). Intra-axial brain lesions were manually segmented in patients with MRE seen at our institution (EL_INST). These lesions were then normalized to standard space and used as seeds in a high-resolution normative resting state functional magnetic resonance imaging template. The resulting connectivity maps were first thresholded (p_{Bonferroni_cor} < 0.05) and binarized; the thresholded binarized connectivity maps were subsequently summed to produce overall group connectivity maps, which were compared with established resting-state networks to identify potential networks prone to epileptogenicity. To validate our data, this approach was also applied to an external dataset of epileptogenic lesions identified from the literature (EL_LIT). As an additional exploratory analysis, we also segmented and computed the connectivity of institutional non-epileptogenic lesions (NEL_INST), calculating voxel-wise odds ratios (VORs) to identify voxels more likely to be functionally-connected with EL_INST versus NEL_INST. To ensure connectivity results were not driven by anatomical overlap, the extent of lesion overlap between EL_INST, and EL_LIT and NEL_INST was assessed using the Dice Similarity Coefficient (DSC, lower index ~ less overlap). Twenty-eight patients from our institution were included (EL_INST: 17 patients, 17 lesions, 10 low-grade glioma, 3 cavernoma, 4 focal cortical dysplasia; NEL_INST: 11 patients, 33 lesions, all brain metastases). An additional 23 cases (25 lesions) with similar characteristics to the EL_INST data were identified from the literature (EL_LIT). Despite minimal anatomical overlap of lesions, both EL_INST and EL_LIT showed greatest functional connectivity overlap with structures in the Default Mode Network, Frontoparietal Network, Ventral Attention Network, and the Limbic Network-with percentage volume overlap of 19.5%, 19.1%, 19.1%, and 12.5%, respectively-suggesting them as networks consistently engaged by epileptogenic mass lesions. Our exploratory analysis moreover showed that the mesial frontal lobes, parahippocampal gyrus, and lateral temporal neocortex were at least twice as likely to be functionally connected with the EL_INST compared to the NEL_INST group (i.e. Peak VOR > 2.0); canonical resting-state networks preferentially engaged by EL_INSTs were the Limbic and the Frontoparietal Networks (Mean VOR > 1.5). In this proof of concept study, we demonstrate the feasibility of LNM for intra-axial mass lesions by showing that ELs have discrete functional connections and may preferentially engage in discrete resting-state networks. Thus, the underlying normative neural circuitry may, in part, explain the propensity of particular lesions toward the development of MRE. If prospectively validated, this has ramifications for patient counseling along with both approach and timing of surgery for lesions in locations prone to development of MRE.

Magnetoencephalography resting state connectivity patterns as indicatives of surgical outcome in epilepsy patients

OBJECTIVE

Focal epilepsy is a disorder affecting several brain networks; however, epilepsy surgery usually targets a restricted region, the so-called epileptic focus. There is a growing interest in embedding resting state (RS) connectivity analysis into pre-surgical workup.

APPROACH

In this retrospective study, we analyzed Magnetoencephalography (MEG) long-range RS functional connectivity patterns in patients with drug-resistant focal epilepsy. MEG recorded prior to surgery from seven seizure-free (Engel Ia) and five non seizure-free (Engel III or IV) patients were analyzed (minimum 2-years post-surgical follow-up). MEG segments without any detectable epileptic activity were source localized using wavelet-based Maximum Entropy on the Mean method. Amplitude envelope correlation in the theta (4-8 Hz), alpha (8-13 Hz), and beta (13-26 Hz) bands were used for assessing connectivity.

MAIN RESULTS

For seizure-free patients, we found an isolated epileptic network characterized by weaker connections between the brain region where interictal epileptic discharges (IED) are generated and the rest of the cortex, when compared to connectivity between the corresponding contralateral homologous region and the rest of the cortex. Contrarily, non seizure-free patients exhibited a widespread RS epileptic network characterized by stronger connectivity between the IED generator and the rest of the cortex, in comparison to the contralateral region and the cortex. Differences between the two seizure outcome groups concerned mainly distant long-range connections and were found in the alpha-band.

SIGNIFICANCE

Importantly, these connectivity patterns suggest specific mechanisms describing the underlying organization of the epileptic network and were detectable at the individual patient level, supporting the prospect use of MEG connectivity patterns in epilepsy to predict post-surgical seizure outcome.

Longitudinal assessment of resting-state fMRI in temporal lobe epilepsy: A two-year follow-up study

In this study, we aimed to detect longitudinal alterations in local spontaneous brain activity and functional connectivity (FC) of the default mode network (DMN) in patients with temporal lobe epilepsy (TLE) over a two-year follow-up. We used amplitude of low-frequency fluctuation (ALFF) analysis and independent component analysis (ICA) to explore differences in local spontaneous brain activity and FC strength. In total, 33 participants (16 patients with TLE and 17 age- and gender-matched healthy controls (HCs)) were recruited in this study. All participants performed the Attention Network Test (ANT) for evaluation of the executive control function. Compared with healthy patients at baseline, patients with TLE at follow-up exhibited increased ALFF values in the left medial frontal gyrus, as well as reduced FC values in the left inferior parietal gyrus (IPG) within the DMN. Patients with TLE revealed executive dysfunction, but no progressive deterioration was observed during follow-up. This study revealed the abnormal distribution of ALFF values and Rs-FC changes over a two-year follow-up period in TLE, both of which demonstrated different reorganization trajectories and loss of efficiency.

Lesion Network Localization of Seizure Freedom following MR-guided Laser Interstitial Thermal Ablation

Treatment-resistant epilepsy is a common and debilitating neurological condition, for which neurosurgical cure is possible. Despite undergoing nearly identical ablation procedures however, individuals with treatment-resistant epilepsy frequently exhibit heterogeneous outcomes. We hypothesized that treatment response may be related to the brain regions to which MR-guided laser ablation volumes are functionally connected. To test this, we mapped the resting-state functional connectivity of surgical ablations that either resulted in seizure freedom (N = 11) or did not result in seizure freedom (N = 16) in over 1,000 normative connectomes. There was no difference seizure outcome with respect to the anatomical location of the ablations, and very little overlap between ablation areas was identified using the Dice Index. Ablations that did not result in seizure-freedom were preferentially connected to a number of cortical and subcortical regions, as well as multiple canonical resting-state networks. In contrast, ablations that led to seizure-freedom were more functionally connected to prefrontal cortices. Here, we demonstrate that underlying normative neural circuitry may in part explain heterogenous outcomes following ablation procedures in different brain regions. These findings may ultimately inform target selection for ablative epilepsy surgery based on normative intrinsic connectivity of the targeted volume.

fMRI functional connectivity as an indicator of interictal epileptic discharges

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Functional connectivity to successful EEG/fMRI response indicates higher IED rates.

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The results hold when correcting for distance to maximum EEG-fMRI and laterality.

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Intracranial IED rates correlate with functional connectivity to EEG/fMRI maxima.

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Results suggest it is feasible to non-invasively infer the brain regions with IEDs.

Objective

To explore the relationship between functional connectivity and presence of interictal epileptic discharges (IEDs) in different brain regions in intracranial EEG (iEEG).

Methods

We studied 38 focal epilepsy patients who underwent simultaneous EEG/fMRI scanning and subsequent intracerebral stereo-EEG investigation. In EEG/fMRI analysis, IEDs with different spatial distributions were considered independent studies and IED-related maximal BOLD responses were evaluated. Studies with iEEG electrodes inside the maximal responses were selected and divided into three groups: Studies with 1. distinct maximal BOLD highly concordant with seizure-onset-zone (SOZ); 2. Moderate maximal BOLD concordant with SOZ; 3. maximal BOLD discordant with SOZ. Using maximal BOLD as seed, its functionally connected zone (FCZ) was determined. IED rates in iEEG channels inside and outside the FCZ were compared in the three groups. The effect of laterality and distance between channels and maximal BOLD, and correlation between functional connectivity values and IED rates were analyzed.

Results

Thirty-six studies in 25 patients were included. IED rates of intracranial EEG channels inside the FCZ were significantly higher than outside in Group 1 (p = 2.6×10−6) and Group 2 (p = 1.2×10−3) and the inside-outside difference remained after regressing distance and laterality factors. In Group 1, connectivity values were significantly correlated with IED rates in channels inside the FCZ (p < 0.05).

Significance

Our results indicate a higher probability of finding intracranial IEDs in the FCZ of SOZ-concordant maximal BOLD responses than in other regions, regardless of distance and laterality. In studies with distinct maximal BOLD, connectivity values can partially predict IED rates in intracranial EEG. It is thus feasible to non-invasively delineate brain regions that are likely to have high IED rates.