Patterns of altered functional connectivity in mesial temporal lobe epilepsy

Improved Dipole Source Localization from Simultaneous MEG-EEG Data by Combining a Global Optimization Algorithm with a Local Parameter Search: A Brain Phantom Study

Dipole localization, a fundamental challenge in electromagnetic source imaging, inherently constitutes an optimization problem aimed at solving the inverse problem of electric current source estimation within the human brain. The accuracy of dipole localization algorithms is contingent upon the complexity of the forward model, often referred to as the head model, and the signal-to-noise ratio (SNR) of measurements. In scenarios characterized by low SNR, often corresponding to deep-seated sources, existing optimization techniques struggle to converge to global minima, thereby leading to the localization of dipoles at erroneous positions, far from their true locations. This study presents a novel hybrid algorithm that combines simulated annealing with the traditional quasi-Newton optimization method, tailored to address the inherent limitations of dipole localization under low-SNR conditions. Using a realistic head model for both electroencephalography (EEG) and magnetoencephalography (MEG), it is demonstrated that this novel hybrid algorithm enables significant improvements of up to 45% in dipole localization accuracy compared to the often-used dipole scanning and gradient descent techniques. Localization improvements are not only found for single dipoles but also in two-dipole-source scenarios, where sources are proximal to each other. The novel methodology presented in this work could be useful in various applications of clinical neuroimaging, particularly in cases where recordings are noisy or sources are located deep within the brain.

Connectivity of the Piriform Cortex and its Implications in Temporal Lobe Epilepsy

Background

The piriform cortex has been implicated in the initiation, spread and termination of epileptic seizures. This understanding has extended to surgical management of epilepsy, where it has been shown that resection or ablation of the piriform cortex can result in better outcomes. How and why the piriform cortex may play such a crucial role in seizure networks is not well understood. To answer these questions, we investigated the functional and structural connectivity of the piriform cortex in both healthy controls and temporal lobe epilepsy (TLE) patients.

Methods

We studied a retrospective cohort of 55 drug-resistant unilateral TLE patients and 26 healthy controls who received structural and functional neuroimaging. Using seed-to-voxel connectivity we compared the normative whole-brain connectivity of the piriform to that of the hippocampus, a region commonly involved in epilepsy, to understand the differential contribution of the piriform to the epileptogenic network. We subsequently measured the inter-piriform coupling (IPC) to quantify similarities in the inter-hemispheric cortical functional connectivity profile between the two piriform cortices. We related differences in IPC in TLE back to aberrations in normative piriform connectivity, whole brain functional properties, and structural connectivity.

Results

We find that relative to the hippocampus, the piriform is functionally connected to the anterior insula and the rest of the salience ventral attention network (SAN). We also find that low IPC is a sensitive metric of poor surgical outcome (sensitivity: 85.71%, 95% CI: [19.12%, 99.64%]); and differences in IPC within TLE were related to disconnectivity and hyperconnectivity to the anterior insula and the SAN. More globally, we find that low IPC is associated with whole-brain functional and structural segregation, marked by decreased functional small-worldness and fractional anisotropy.

Conclusions

Our study presents novel insights into the functional and structural neural network alterations associated with this structure, laying the foundation for future work to carefully consider its connectivity during the presurgical management of epilepsy.

Causal Cortical and Thalamic Connections in the Human Brain

The brain's functional architecture is intricately shaped by causal connections between its cortical and subcortical structures. Here, we studied 27 participants with 4864 electrodes implanted across the anterior, mediodorsal, and pulvinar thalamic regions, and the cortex. Using data from electrical stimulation procedures and a data-driven approach informed by neurophysiological standards, we dissociated three unique spectral patterns generated by the perturbation of a given brain area. Among these, a novel waveform emerged, marked by delayed-onset slow oscillations in both ipsilateral and contralateral cortices following thalamic stimulations, suggesting a mechanism by which a thalamic site can influence bilateral cortical activity. Moreover, cortical stimulations evoked earlier signals in the thalamus than in other connected cortical areas suggesting that the thalamus receives a copy of signals before they are exchanged across the cortex. Our causal connectivity data can be used to inform biologically-inspired computational models of the functional architecture of the brain.

Breath-Hold-Triggered BOLD fMRI in Drug-Resistant Nonlesional Focal Epilepsy-A Pilot Study

PURPOSE

Individuals with drug-resistant epilepsy may benefit from epilepsy surgery. In nonlesional cases, where no epileptogenic lesion can be detected on structural magnetic resonance imaging, multimodal neuroimaging studies are required. Breath-hold-triggered BOLD fMRI (bh-fMRI) was developed to measure cerebrovascular reactivity in stroke or angiopathy and highlights regional network dysfunction by visualizing focal impaired flow increase after vasodilatory stimulus. This regional dysfunction may correlate with the epileptogenic zone. In this prospective single-center single-blind pilot study, we aimed to establish the feasibility and safety of bh-fMRI in individuals with drug-resistant non-lesional focal epilepsy undergoing presurgical evaluation.

METHODS

In this prospective study, 10 consecutive individuals undergoing presurgical evaluation for drug-resistant focal epilepsy were recruited after case review at a multidisciplinary patient management conference. Electroclinical findings and results of other neuroimaging were used to establish the epileptogenic zone hypothesis. To calculate significant differences in cerebrovascular reactivity in comparison to the normal population, bh-fMRIs of 16 healthy volunteers were analyzed. The relative flow change of each volume of interest (VOI) of the atlas was then calculated compared to the flow change of the whole brain resulting in an atlas of normal cerebral reactivity. Consequently, the mean flow change of every VOI of each patient was tested against the healthy volunteers group. Areas with significant impairment of cerebrovascular reactivity had decreased flow change and were compared to the epileptogenic zone localization hypothesis in a single-blind design.

RESULTS

Acquisition of bh-fMRI was feasible in 9/10 cases, with one patient excluded due to noncompliance with breathing maneuvers. No adverse events were observed, and breath-hold for intermittent hypercapnia was well tolerated. On blinded review, we observed full or partial concordance of the local network dysfunction seen on bh-fMRI with the electroclinical hypothesis in 6/9 cases, including cases with extratemporal lobe epilepsy and those with nonlocalizing 18F-fluorodeoxyglucose positron emission tomography (FDG-PET).

CONCLUSION

This represents the first report of bh-fMRI in individuals with epilepsy undergoing presurgical evaluation. We found bh-fMRI to be feasible and safe, with a promising agreement to electroclinical findings. Thus, bh-fMRI may represent a potential modality in the presurgical evaluation of epilepsy. Further studies are needed to establish clinical utility.

Causal Cortical and Thalamic Connections in the Human Brain

The brain's functional architecture is intricately shaped by causal connections between its cortical and subcortical structures. Here, we studied 27 participants with 4864 electrodes implanted across the anterior, mediodorsal, and pulvinar thalamic regions, and the cortex. Using data from electrical stimulation procedures and a data-driven approach informed by neurophysiological standards, we dissociated three unique spectral patterns generated by the perturbation of a given brain area. Among these, a novel waveform emerged, marked by delayed-onset slow oscillations in both ipsilateral and contralateral cortices following thalamic stimulations, suggesting a mechanism by which a thalamic site can influence bilateral cortical activity. Moreover, cortical stimulations evoked earlier signals in the thalamus than in other connected cortical areas suggesting that the thalamus receives a copy of signals before they are exchanged across the cortex. Our causal connectivity data can be used to inform biologically-inspired computational models of the functional architecture of the brain.

Topological Embedding of Human Brain Networks with Applications to Dynamics of Temporal Lobe Epilepsy

We introduce a novel, data-driven topological data analysis (TDA) approach for embedding brain networks into a lower-dimensional space in quantifying the dynamics of temporal lobe epilepsy (TLE) obtained from resting-state functional magnetic resonance imaging (rs-fMRI). This embedding facilitates the orthogonal projection of 0D and 1D topological features, allowing for the visualization and modeling of the dynamics of functional human brain networks in a resting state. We then quantify the topological disparities between networks to determine the coordinates for embedding. This framework enables us to conduct a coherent statistical inference within the embedded space. Our results indicate that brain network topology in TLE patients exhibits increased rigidity in 0D topology but more rapid flections compared to that of normal controls in 1D topology.

Postoperative cerebellar mutism syndrome is an acquired autism-like network disturbance

BACKGROUND

Cerebellar mutism syndrome (CMS) is a common and debilitating complication of posterior fossa tumor surgery in children. Affected children exhibit communication and social impairments that overlap phenomenologically with subsets of deficits exhibited by children with Autism spectrum disorder (ASD). Although both CMS and ASD are thought to involve disrupted cerebro-cerebellar circuitry, they are considered independent conditions due to an incomplete understanding of their shared neural substrates.

METHODS

In this study, we analyzed postoperative cerebellar lesions from 90 children undergoing posterior fossa resection of medulloblastoma, 30 of whom developed CMS. Lesion locations were mapped to a standard atlas, and the networks functionally connected to each lesion were computed in normative adult and pediatric datasets. Generalizability to ASD was assessed using an independent cohort of children with ASD and matched controls (n = 427).

RESULTS

Lesions in children who developed CMS involved the vermis and inferomedial cerebellar lobules. They engaged large-scale cerebellothalamocortical circuits with a preponderance for the prefrontal and parietal cortices in the pediatric and adult connectomes, respectively. Moreover, with increasing connectomic age, CMS-associated lesions demonstrated stronger connectivity to the midbrain/red nuclei, thalami and inferior parietal lobules and weaker connectivity to the prefrontal cortex. Importantly, the CMS-associated lesion network was independently reproduced in ASD and correlated with communication and social deficits, but not repetitive behaviors.

CONCLUSIONS

Our findings indicate that CMS-associated lesions may result in an ASD-like network disturbance that occurs during sensitive windows of brain development. A common network disturbance between CMS and ASD may inform improved treatment strategies for affected children.

Effects of transcranial magnetic stimulation on the human brain recorded with intracranial electrocorticography

Transcranial magnetic stimulation (TMS) is increasingly used as a noninvasive technique for neuromodulation in research and clinical applications, yet its mechanisms are not well understood. Here, we present the neurophysiological effects of TMS using intracranial electrocorticography (iEEG) in neurosurgical patients. We first evaluated safety in a gel-based phantom. We then performed TMS-iEEG in 22 neurosurgical participants with no adverse events. We next evaluated intracranial responses to single pulses of TMS to the dorsolateral prefrontal cortex (dlPFC) (N = 10, 1414 electrodes). We demonstrate that TMS is capable of inducing evoked potentials both locally within the dlPFC and in downstream regions functionally connected to the dlPFC, including the anterior cingulate and insular cortex. These downstream effects were not observed when stimulating other distant brain regions. Intracranial dlPFC electrical stimulation had similar timing and downstream effects as TMS. These findings support the safety and promise of TMS-iEEG in humans to examine local and network-level effects of TMS with higher spatiotemporal resolution than currently available methods.

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.

Efficacy and Safety of Pulse Intravenous Methylprednisolone in Pediatric Epileptic Encephalopathies: Timing and Networks Consideration

Background: Epileptic encephalopathies (EE) are characterized by severe drug-resistant seizures, early onset, and unfavorable developmental outcomes. This article discusses the use of intravenous methylprednisolone (IVMP) pulse therapy in pediatric patients with EE to evaluate its efficacy and tolerability. Methods: This is a retrospective study from 2020 to 2023. Inclusion criteria were ≤18 years at the time of IVMP pulse therapy and at least 6 months of follow-up. Efficacy and outcome, defined as seizure reduction > 50% (responder rate), were evaluated at 6 and 9 months of therapy, and 6 months after therapy suspension; quality of life (QoL) was also assessed. Variables predicting positive post-IVMP outcomes were identified using statistical analysis. Results: The study included 21 patients, with a responder rate of 85.7% at 6 and 9 months of therapy, and 80.9% at 6 months after therapy suspension. Variables significantly predicting favorable outcome were etiology (p = 0.0475) and epilepsy type (p = 0.0475), with the best outcome achieved in patients with genetic epilepsy and those with encephalopathy related to electrical status epilepticus during slow-wave sleep (ESES). All patients evidenced improvements in QoL at the last follow-up, with no relevant adverse events reported. Conclusions: Our study confirmed the efficacy and high tolerability of IVMP pulse therapy in pediatric patients with EE. Genetic epilepsy and ESES were positive predictors of a favorable clinical outcome. QOL, EEG tracing, and postural-motor development showed an improving trend as well. IVMP pulse therapy should be considered earlier in patients with EE.

Direct cortical stimulation induces short-term plasticity of neural oscillations in humans

Patterned brain stimulation is commonly employed as a tool for eliciting plasticity in brain circuits and treating neuropsychiatric disorders. Although widely used in clinical settings, there remains a limited understanding of how stimulation-induced plasticity influences neural oscillations and their interplay with the underlying baseline functional architecture. To address this question, we applied 15 minutes of 10Hz focal electrical simulation, a pattern identical to 'excitatory' repetitive transcranial magnetic stimulation (rTMS), to 14 medically-intractable epilepsy patients undergoing intracranial electroencephalographic (iEEG). We quantified the spectral features of the cortico-cortical evoked potential (CCEPs) in these patients before and after stimulation. We hypothesized that for a given region the temporal and spectral components of the CCEP predicted the location and degree of stimulation-induced plasticity. Across patients, low frequency power (alpha and beta) showed the broadest change, while the magnitude of change was stronger in high frequencies (beta and gamma). Next we demonstrated that regions with stronger baseline evoked spectral responses were more likely to undergo plasticity after stimulation. These findings were specific to a given frequency in a specific temporal window. Post-stimulation power changes were driven by the interaction between direction of change in baseline power and temporal window of change. Finally, regions exhibiting early increases and late decreases in evoked baseline power exhibited power changes after stimulation and were independent of stimulation location. Together, these findings that time-frequency baseline features predict post-stimulation plasticity effects demonstrate properties akin to Hebbian learning in humans and extend this theory to the temporal and spectral window of interest. These findings can help improve our understanding of human brain plasticity and lead to more effective brain stimulation techniques.

Significance Statement

Brain stimulation is increasingly used to treat neuropsychiatric disorders by inducing changes in neural activity at specific brain regions. Despite their effectiveness, how these changes occur, specifically in the spectral domain, is unknown. To better understand how brain oscillations change after patterned stimulation, we performed focused stimulation in epilepsy patients and measured intracranial brain recordings. We found strong and predictable changes in brain oscillations (plasticity) after patterned stimulation. Specifically, low frequencies showing widespread effects and high frequencies exhibiting a greater magnitude of change. These changes were directly related to the temporal and spectral structure of brain responses prior to stimulation. Our study reveals that baseline brain activity patterns can predict how stimulation will induce plasticity in the spectral domain. These findings can help improve our understanding of human brain plasticity and lead to more effective brain stimulation techniques.

Highlights

We applied 15 minutes of repetitive 10Hz focal electrical stimulation and assessed the evoked brain-wide spectral changes with intracranial EEG.10Hz stimulation induced short-term plasticity in low frequency alpha evoked power broadly across regions and time windows and high frequency (beta, gamma) power specifically in early evoked time windows (10-50ms).Across patients, frequency bands, and time windows, brain regions with stronger baseline evoked power were more likely to undergo greater spectral changes after 10Hz stimulation.Post-stimulation spectral changes were specific; that is, for a given frequency band in a specific time window, baseline evoked power predicted post-stimulation change in the same frequency band and time window.Post-stimulation spectral change was driven by an interaction between direction of change and temporal window of baseline power; that is, regions exhibiting baseline evoked early (10-100ms) increases and late (100-200ms) decreases in power correlated with observed post-stimulation spectral changes.These results were independent of stimulation location.

Dissociable default-mode subnetworks subserve childhood attention and cognitive flexibility: Evidence from deep learning and stereotactic electroencephalography

Cognitive flexibility encompasses the ability to efficiently shift focus and forms a critical component of goal-directed attention. The neural substrates of this process are incompletely understood in part due to difficulties in sampling the involved circuitry. We leverage stereotactic intracranial recordings to directly resolve local-field potentials from otherwise inaccessible structures to study moment-to-moment attentional activity in children with epilepsy performing a flexible attentional task. On an individual subject level, we employed deep learning to decode neural features predictive of task performance indexed by single-trial reaction time. These models were subsequently aggregated across participants to identify predictive brain regions based on AAL atlas and FIND functional network parcellations. Through this approach, we show that fluctuations in beta (12-30 Hz) and gamma (30-80 Hz) power reflective of increased top-down attentional control and local neuronal processing within relevant large-scale networks can accurately predict single-trial task performance. We next performed connectomic profiling of these highly predictive nodes to examine task-related engagement of distributed functional networks, revealing exclusive recruitment of the dorsal default mode network during shifts in attention. The identification of distinct substreams within the default mode system supports a key role for this network in cognitive flexibility and attention in children. Furthermore, convergence of our results onto consistent functional networks despite significant inter-subject variability in electrode implantations supports a broader role for deep learning applied to intracranial electrodes in the study of human attention.

Abnormal functional connectivity of the posterior hypothalamus and other arousal regions in surgical temporal lobe epilepsy

OBJECTIVE

This study sought to characterize resting-state functional MRI (fMRI) connectivity patterns of the posterior hypothalamus (pHTH) and the nucleus basalis of Meynert (NBM) in surgical patients with mesial temporal lobe epilepsy (mTLE), and to investigate potential correlations between functional connectivity of these arousal regions and neurocognitive performance.

METHODS

The study evaluated resting-state fMRI in 60 patients with preoperative mTLE and in 95 healthy controls. The authors first conducted voxel-wise connectivity analyses seeded from the pHTH, combined anterior and tuberal hypothalamus (atHTH; i.e., the rest of the hypothalamus), and the NBM ipsilateral (ipsiNBM) and contralateral (contraNBM) to the epileptogenic zone. Based on these results, the authors included the pHTH, ipsiNBM, and frontoparietal neocortex in a network-based statistic (NBS) analysis to elucidate a network that best distinguishes patients from controls. The connections involving the pHTH and ipsiNBM from this network were included in age-corrected pairwise region of interest (ROI) analysis, along with connections between arousal structures, including the pHTH, ipsiNBM, and brainstem arousal regions. Finally, patient functional connectivity was correlated with clinical neurocognitive testing scores for IQ as well as attention and concentration tests.

RESULTS

The voxel-wise analysis demonstrated that the pHTH, when compared with the atHTH, showed more widespread functional connectivity decreases in surgical mTLE patients when compared with controls. It was also observed that the ipsiNBM, but not the contraNBM, showed decreased functional connectivity in mTLE. The NBS analysis uncovered a perturbed network of frontoparietal regions, the pHTH, and ipsiNBM that distinguishes patients from controls. Age-corrected ROI analysis revealed functional connectivity decreases between the pHTH and bilateral superior frontal gyri, medial orbitofrontal cortices, rostral anterior cingulate cortices, and inferior parietal cortices in mTLE when compared with controls. For the ipsiNBM, there was reduced connectivity with bilateral medial orbitofrontal and rostral anterior cingulate cortices. Age-corrected ROI analysis also demonstrated upstream connectivity decreases from controls between the pHTH and the brainstem arousal regions, cuneiform/subcuneiform (CSC) nuclei, and ventral tegmental area, as well as the ipsiNBM and CSC nuclei. Reduced functional connectivity was also detected between the pHTH and ipsiNBM. Lastly, neurocognitive test scores for attention and concentration were found to be positively correlated with the functional connectivity between the pHTH and ipsiNBM, suggesting worse performance associated with connectivity perturbations.

CONCLUSIONS

This study demonstrated perturbed resting-state functional connectivity of arousal regions in surgical mTLE and is one of the first investigations to demonstrate decreased functional connectivity of the pHTH with frontoparietal regions and other arousal regions. Connectivity disturbances in arousal regions may contribute to neurocognitive deficits in surgical mTLE patients.

Magnetoencephalography-based approaches to epilepsy classification

Epilepsy is a chronic central nervous system disorder characterized by recurrent seizures. Not only does epilepsy severely affect the daily life of the patient, but the risk of premature death in patients with epilepsy is three times higher than that of the normal population. Magnetoencephalography (MEG) is a non-invasive, high temporal and spatial resolution electrophysiological data that provides a valid basis for epilepsy diagnosis, and used in clinical practice to locate epileptic foci in patients with epilepsy. It has been shown that MEG helps to identify MRI-negative epilepsy, contributes to clinical decision-making in recurrent seizures after previous epilepsy surgery, that interictal MEG can provide additional localization information than scalp EEG, and complete excision of the stimulation area defined by the MEG has prognostic significance for postoperative seizure control. However, due to the complexity of the MEG signal, it is often difficult to identify subtle but critical changes in MEG through visual inspection, opening up an important area of research for biomedical engineers to investigate and implement intelligent algorithms for epilepsy recognition. At the same time, the use of manual markers requires significant time and labor costs, necessitating the development and use of computer-aided diagnosis (CAD) systems that use classifiers to automatically identify abnormal activity. In this review, we discuss in detail the results of applying various different feature extraction methods on MEG signals with different classifiers for epilepsy detection, subtype determination, and laterality classification. Finally, we also briefly look at the prospects of using MEG for epilepsy-assisted localization (spike detection, high-frequency oscillation detection) due to the unique advantages of MEG for functional area localization in epilepsy, and discuss the limitation of current research status and suggestions for future research. Overall, it is hoped that our review will facilitate the reader to quickly gain a general understanding of the problem of MEG-based epilepsy classification and provide ideas and directions for subsequent research.

Altered metabolic-functional coupling in the epileptogenic network could predict surgical outcomes of mesial temporal lobe epilepsy

Objective

To investigate the relationship between glucose metabolism and functional activity in the epileptogenic network of patients with mesial temporal lobe epilepsy (MTLE) and to determine whether this relationship is associated with surgical outcomes.

Methods

¹⁸F-FDG PET and resting-state functional MRI (rs-fMRI) scans were performed on a hybrid PET/MR scanner in 38 MTLE patients with hippocampal sclerosis (MR-HS), 35 MR-negative patients and 34 healthy controls (HC). Glucose metabolism was measured using ¹⁸F-FDG PET standardized uptake value ratio (SUVR) relative to cerebellum; Functional activity was obtained by fractional amplitude of low-frequency fluctuation (fALFF). The betweenness centrality (BC) of metabolic covariance network and functional network were calculated using graph theoretical analysis. Differences in SUVR, fALFF, BC and the spatial voxel-wise SUVR-fALFF couplings of the epileptogenic network, consisting of default mode network (DMN) and thalamus, were evaluated by Mann-Whitney U test (using the false discovery rate [FDR] for multiple comparison correction). The top ten SUVR-fALFF couplings were selected by Fisher score to predict surgical outcomes using logistic regression model.

Results

The results showed decreased SUVR-fALFF coupling in the bilateral middle frontal gyrus (P_FDR = 0.0230, P_FDR = 0.0296) in MR-HS patients compared to healthy controls. Coupling in the ipsilateral hippocampus was marginally increased (P_FDR = 0.0802) in MR-HS patients along with decreased BC of metabolic covariance network and functional network (P_FDR = 0.0152; P_FDR = 0.0429). With Fisher score ranking, the top ten SUVR-fALFF couplings in regions from DMN and thalamic subnuclei could predict surgical outcomes with the best performance being a combination of ten SUVR-fALFF couplings with an AUC of 0.914.

Conclusion

These findings suggest that the altered neuroenergetic coupling in the epileptogenic network is associated with surgical outcomes of MTLE patients, which may provide insight into their pathogenesis and help with preoperative evaluation.

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.

Advances in Image Processing for Epileptogenic Zone Detection with MRI

Focal epilepsy is a common and severe neurologic disorder. Neuroimaging aims to identify the epileptogenic zone (EZ), preferably as a macroscopic structural lesion. For approximately a third of patients with chronic drug-resistant focal epilepsy, the EZ cannot be precisely identified using standard 3.0-T MRI. This may be due to either the EZ being undetectable at imaging or the seizure activity being caused by a physiologic abnormality rather than a structural lesion. Computational image processing has recently been shown to aid radiologic assessments and increase the success rate of uncovering suspicious regions by enhancing their visual conspicuity. While structural image analysis is at the forefront of EZ detection, physiologic image analysis has also been shown to provide valuable information about EZ location. This narrative review summarizes and explains the current state-of-the-art computational approaches for image analysis and presents their potential for EZ detection. Current limitations of the methods and possible future directions to augment EZ detection are discussed.

Atypical connectome topography and signal flow in temporal lobe epilepsy

Temporal lobe epilepsy (TLE) is one of the most common pharmaco-resistant epilepsies in adults. While hippocampal pathology is the hallmark of this condition, emerging evidence indicates that brain alterations extend beyond the mesiotemporal epicenter and affect macroscale brain function and cognition. We studied macroscale functional reorganization in TLE, explored structural substrates, and examined cognitive associations. We investigated a multisite cohort of 95 patients with pharmaco-resistant TLE and 95 healthy controls using state-of-the-art multimodal 3T magnetic resonance imaging (MRI). We quantified macroscale functional topographic organization using connectome dimensionality reduction techniques and estimated directional functional flow using generative models of effective connectivity. We observed atypical functional topographies in patients with TLE relative to controls, manifesting as reduced functional differentiation between sensory/motor networks and transmodal systems such as the default mode network, with peak alterations in bilateral temporal and ventromedial prefrontal cortices. TLE-related topographic changes were consistent in all three included sites and reflected reductions in hierarchical flow patterns between cortical systems. Integration of parallel multimodal MRI data indicated that these findings were independent of TLE-related cortical grey matter atrophy, but mediated by microstructural alterations in the superficial white matter immediately beneath the cortex. The magnitude of functional perturbations was robustly associated with behavioral markers of memory function. Overall, this work provides converging evidence for macroscale functional imbalances, contributing microstructural alterations, and their associations with cognitive dysfunction in TLE.

Differences in the Default Mode Network of Temporal Lobe Epilepsy Patients Detected by Hilbert-Huang Transform Based Dynamic Functional Connectivity

Resting-state functional connectivity, constructed via functional magnetic resonance imaging, has become an essential tool for exploring brain functions. Aside from the methods focusing on the static state, investigating dynamic functional connectivity can better uncover the fundamental properties of brain networks. Hilbert-Huang transform (HHT) is a novel time-frequency technique that can adapt to both non-linear and non-stationary signals, which may be an effective tool for investigating dynamic functional connectivity. To perform the present study, we investigated time-frequency dynamic functional connectivity among 11 brain regions of the default mode network by first projecting the coherence into the time and frequency domains, and subsequently by identifying clusters in the time-frequency domain using k-means clustering. Experiments on 14 temporal lobe epilepsy (TLE) patients and 21 age and sex-matched healthy controls were performed. The results show that functional connections in the brain regions of the hippocampal formation, parahippocampal gyrus, and retrosplenial cortex (Rsp) were reduced in the TLE group. However, the connections in the brain regions of the posterior inferior parietal lobule, ventral medial prefrontal cortex, and the core subsystem could hardly be detected in TLE patients. The findings not only demonstrate the feasibility of utilizing HHT in dynamic functional connectivity for epilepsy research, but also indicate that TLE may cause damage to memory functions, disorders of processing self-related tasks, and impairment of constructing a mental scene.

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.

Multilayer brain networks can identify the epileptogenic zone and seizure dynamics

Seizure generation, propagation, and termination occur through spatiotemporal brain networks. In this paper, we demonstrate the significance of large-scale brain interactions in high-frequency (80-200Hz) for the identification of the epileptogenic zone (EZ) and seizure evolution. To incorporate the continuity of neural dynamics, here we have modeled brain connectivity constructed from stereoelectroencephalography (SEEG) data during seizures using multilayer networks. After introducing a new measure of brain connectivity for temporal networks, named multilayer eigenvector centrality (mlEVC), we applied a consensus hierarchical clustering on the developed model to identify the EZ as a cluster of nodes with distinctive brain connectivity in the ictal period. Our algorithm could successfully predict electrodes inside the resected volume as EZ for 88% of participants, who all were seizure-free for at least 12 months after surgery. Our findings illustrated significant and unique desynchronization between EZ and the rest of the brain in the early to mid-seizure. We showed that aging and the duration of epilepsy intensify this desynchronization, which can be the outcome of abnormal neuroplasticity. Additionally, we illustrated that seizures evolve with various network topologies, confirming the existence of different epileptogenic networks in each patient. Our findings suggest not only the importance of early intervention in epilepsy but possible factors that correlate with disease severity. Moreover, by analyzing the propagation patterns of different seizures, we demonstrate the necessity of collecting sufficient data for identifying epileptogenic networks.

Local neurodynamics as a signature of cortical areas: new insights from sleep

Sleep crucial for the animal survival is accompanied by huge changes in neuronal electrical activity over time, the neurodynamics. Here, drawing on intracranial stereo-electroencephalographic (sEEG) recordings from the Montreal Neurological Institute (MNI), we analyzed local neurodynamics in the waking state at rest and during the N2, N3, and rapid eye movement (REM) sleep phases. Higuchi fractal dimension (HFD)-a measure of signal complexity-was studied as a feature of the local neurodynamics of the primary motor (M1), somatosensory (S1), and auditory (A1) cortices. The key working hypothesis, that the relationships between local neurodynamics preserve in all sleep phases despite the neurodynamics complexity reduces in sleep compared with wakefulness, was supported by the results. In fact, while HFD awake > REM > N2 > N3 (P < 0.001 consistently), HFD in M1 > S1 > A1 in awake and all sleep stages (P < 0.05 consistently). Also power spectral density was studied for consistency with previous investigations. Meaningfully, we found a local specificity of neurodynamics, well quantified by the fractal dimension, expressed in wakefulness and during sleep. We reinforce the idea that neurodynamic may become a new criterion for cortical parcellation, prospectively improving the understanding and ability of compensatory interventions for behavioral disorders.

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.

Functional anomaly mapping lateralizes temporal lobe epilepsy with high accuracy in individual patients

Mesial temporal lobe epilepsy (mTLE) is associated with variable dysfunction beyond the temporal lobe. We used functional anomaly mapping (FAM), a multivariate machine learning approach to resting state fMRI analysis to measure subcortical and cortical functional aberrations in patients with mTLE. We also examined the value of individual FAM in lateralizing the hemisphere of seizure onset in mTLE patients. Methods: Patients and controls were selected from an existing imaging and clinical database. After standard preprocessing of resting state fMRI, time-series were extracted from 400 cortical and 32 subcortical regions of interest (ROIs) defined by atlases derived from functional brain organization. Group-level aberrations were measured by contrasting right (RTLE) and left (LTLE) patient groups to controls in a support vector regression models, and tested for statistical reliability using permutation analysis. Individualized functional anomaly maps (FAMs) were generated by contrasting individual patients to the control group. Half of patients were used for training a classification model, and the other half for estimating the accuracy to lateralize mTLE based on individual FAMs. Results: Thirty-two right and 14 left mTLE patients (33 with evidence of hippocampal sclerosis on MRI) and 94 controls were included. At group levels, cortical regions affiliated with limbic and somatomotor networks were prominent in distinguishing RTLE and LTLE from controls. At individual levels, most TLE patients had high anomaly in bilateral mesial temporal and medial parietooccipital default mode regions. A linear support vector machine trained on 50% of patients could accurately lateralize mTLE in remaining patients (median AUC =1.0 [range 0.97-1.0], median accuracy = 96.87% [85.71-100Significance: Functional anomaly mapping confirms widespread aberrations in function, and accurately lateralizes mTLE from resting state fMRI. Future studies will evaluate FAM as a non-invasive localization method in larger datasets, and explore possible correlations with clinical characteristics and disease course.

Resting-State fMRI Can Detect Alterations in Seizure Onset and Spread Regions in Patients with Non-Lesional Epilepsy: A Pilot Study

Introduction

Epilepsy is defined as non-lesional (NLE) when a lesion cannot be localized via standard neuroimaging. NLE is known to have a poor response to surgery. Stereotactic electroencephalography (sEEG) can detect functional connectivity (FC) between zones of seizure onset (OZ) and early (ESZ) and late (LSZ) spread. We examined whether resting-state fMRI (rsfMRI) can detect FC alterations in NLE to see whether noninvasive imaging techniques can localize areas of seizure propagation to potentially target for intervention.

Methods

This is a retrospective study of 8 patients with refractory NLE who underwent sEEG electrode implantation and 10 controls. The OZ, ESZ, and LSZ were identified by generating regions around sEEG contacts that recorded seizure activity. Amplitude synchronization analysis was used to detect the correlation of the OZ to the ESZ. This was also done using the OZ and ESZ of each NLE patient for each control. Patients with NLE were compared to controls individually using Wilcoxon tests and as a group using Mann-Whitney tests. Amplitude of low-frequency fluctuations (ALFF), fractional ALFF (fALFF), regional homogeneity (ReHo), degree of centrality (DoC), and voxel-mirrored homotopic connectivity (VMHC) were calculated as the difference between NLE and controls and compared between the OZ and ESZ and to zero. A general linear model was used with age as a covariate with Bonferroni correction for multiple comparisons.

Results

Five out of 8 patients with NLE showed decreased correlations from the OZ to the ESZ. Group analysis showed patients with NLE had lower connectivity with the ESZ. Patients with NLE showed higher fALFF and ReHo in the OZ but not the ESZ, and higher DoC in the OZ and ESZ. Our results indicate that patients with NLE show high levels of activity but dysfunctional connections in seizure-related areas.

Discussion

rsfMRI analysis showed decreased connectivity directly between seizure-related areas, while FC metric analysis revealed increases in local and global connectivity in seizure-related areas. FC analysis of rsfMRI can detect functional disruption that may expose the pathophysiology underlying NLE.

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.

Multimodal Triplet Attention Network for Brain Disease Diagnosis

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

Graph-generative neural network for EEG-based epileptic seizure detection via discovery of dynamic brain functional connectivity

Dynamic complexity in brain functional connectivity has hindered the effective use of signal processing or machine learning methods to diagnose neurological disorders such as epilepsy. This paper proposed a new graph-generative neural network (GGN) model for the dynamic discovery of brain functional connectivity via deep analysis of scalp electroencephalogram (EEG) signals recorded from various regions of a patient's scalp. Brain functional connectivity graphs are generated for the extraction of spatial-temporal resolution of various onset epilepsy seizure patterns. Our supervised GGN model was substantiated by seizure detection and classification experiments. We train the GGN model using a clinically proven dataset of over 3047 epileptic seizure cases. The GGN model achieved a 91% accuracy in classifying seven types of epileptic seizure attacks, which outperformed the 65%, 74%, and 82% accuracy in using the convolutional neural network (CNN), graph neural networks (GNN), and transformer models, respectively. We present the GGN model architecture and operational steps to assist neuroscientists or brain specialists in using dynamic functional connectivity information to detect neurological disorders. Furthermore, we suggest to merge our spatial-temporal graph generator design in upgrading the conventional CNN and GNN models with dynamic convolutional kernels for accuracy enhancement.

Distinct limbic connectivity in left and right benign mesial temporal lobe epilepsy: Evidence from a resting state functional MRI study

Background

Functional connectivity (FC) studies showed that pharmaco-resistant mesial temporal lobe epilepsy (MTLE) affects not only the limbic system, but also several extra-limbic regions, including areas belonging to resting state networks. Less is known about FC in subjects with benign MTLE (i.e., sensitive to antiseizure medication, bMTLE).

Aim and methods

We evaluated FC of hippocampus and amygdala in subjects with bMTLE, distinguished based on the epileptic focus lateralization. We enrolled 19 patients (10 with left and 9 with right bMTLE) and 10 age-matched healthy subjects. Connectivity was investigated at rest by using a seed-based regression analyses approach with four regions of interest (left and right hippocampus, left and right amygdala). Patients were also tested with a neuropsychological battery and their scores were correlated with fMRI data.

Results and conclusions

Our study documented an asymmetrical disruption of FC in bMTLE, in relation to the side of the focus. Right subjects only exhibited limited altered connections, while left subjects—who performed worse in verbal memory tests—showed a wide bilateral hypoconnectivity of hippocampus and amygdala with areas belonging to language and memory network. The strength of FC between left limbic areas and language and memory network correlated with better performances in verbal memory tests. Moreover, we observed an increased FC with areas of default mode network, more pronounced in left subjects, a possible attempt to compensate cognitive deficit but without effectiveness.

We believe that these findings could help to better characterize bMTLE, in which a dysfunction of limbic connectivity is detectable despite well-controlled epilepsy.

Altered Resting State Networks Before and After Temporal Lobe Epilepsy Surgery

OBJECTIVES

To explore the resting state networks (RSNs) alterations in patients with unilateral mesial temporal lobe epilepsy (mTLE) before and after successful surgery.

METHODS

Resting-state functional MRI and T1-weighted structural MRI were obtained in 37 mTLE patients who achieved seizure freedom after anterior temporal lobectomy. Patients were scanned before surgery and at two years after surgery. Twenty-eight age- and sex-matched healthy controls were scanned once. Functional connectivity (FC) changes within and between ten common RSNs before and after surgery, and FC changes between hippocampus and RSNs were explored.

RESULTS

Before surgery, decreased FC was found within visual network and basal ganglia network, while after surgery, FC within basal ganglia network further decreased but FC within sensorimotor network and dorsal attention network increased. Before surgery, between-network FC related to basal ganglia network, visual network and dorsal attention network decreased, while between-network FC related to default mode network increased. After surgery, between-network FC related to visual network and dorsal attention network significantly increased. In addition, before surgery, ipsilateral hippocampus showed decreased FC with visual network, basal ganglia network, sensorimotor network, default mode network and frontoparietal network, while contralateral rostral hippocampus showed increased FC with salience network. After surgery, no obvious FC changes were found between contralateral hippocampus and these RSNs.

CONCLUSION

MTLE patients showed significant RSNs alterations before and after surgery. Basal ganglia network showed progressive decline in functional connectivity. Successful surgery may lead to RSNs reorganization. These results provide preliminary evidence for postoperative functional remodeling at whole-brain-network level.

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

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

Mechanisms for Cognitive Impairment in Epilepsy: Moving Beyond Seizures

There has been a major emphasis on defining the role of seizures in the causation of cognitive impairments like memory deficits in epilepsy. Here we focus on an alternative hypothesis behind these deficits, emphasizing the mechanisms of information processing underlying healthy cognition characterized as rate, temporal and population coding. We discuss the role of the underlying etiology of epilepsy in altering neural networks thereby leading to both the propensity for seizures and the associated cognitive impairments. In addition, we address potential treatments that can recover the network function in the context of a diseased brain, thereby improving both seizure and cognitive outcomes simultaneously. This review shows the importance of moving beyond seizures and approaching the deficits from a system-level perspective with the guidance of network neuroscience.

Functional Connectivity of the Brain Across Rodents and Humans

Resting-state functional magnetic resonance imaging (rs-fMRI), which measures the spontaneous fluctuations in the blood oxygen level-dependent (BOLD) signal, is increasingly utilized for the investigation of the brain's physiological and pathological functional activity. Rodents, as a typical animal model in neuroscience, play an important role in the studies that examine the neuronal processes that underpin the spontaneous fluctuations in the BOLD signal and the functional connectivity that results. Translating this knowledge from rodents to humans requires a basic knowledge of the similarities and differences across species in terms of both the BOLD signal fluctuations and the resulting functional connectivity. This review begins by examining similarities and differences in anatomical features, acquisition parameters, and preprocessing techniques, as factors that contribute to functional connectivity. Homologous functional networks are compared across species, and aspects of the BOLD fluctuations such as the topography of the global signal and the relationship between structural and functional connectivity are examined. Time-varying features of functional connectivity, obtained by sliding windowed approaches, quasi-periodic patterns, and coactivation patterns, are compared across species. Applications demonstrating the use of rs-fMRI as a translational tool for cross-species analysis are discussed, with an emphasis on neurological and psychiatric disorders. Finally, open questions are presented to encapsulate the future direction of the field.

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.

Common functional connectivity alterations in focal epilepsies identified by machine learning

OBJECTIVE

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

METHODS

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

RESULTS

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

SIGNIFICANCE

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

Interhemispheric functional connectivity asymmetry is distinctly affected in left and right mesial temporal lobe epilepsy

INTRODUCTION

The differences of functional connectivity (FC) and functional asymmetry between left and right mesial temporal lobe epilepsy with hippocampal sclerosis (LMTLE and RMTLE) have not been completely clarified yet. The purpose of the present study is to investigate the FC changes and the FC asymmetric patterns of MTLE, and to compare the differences in FC and functional asymmetry between LMTLE and RMTLE.

METHODS

In total, 12 LMTLE, 11 RMTLE patients, and 23 healthy controls (HC) were included. Region of interest (ROI)-based analysis was used to evaluate FC. The right functional connectivity (rFC) and left functional connectivity (lFC) of each ROI were calculated. Asymmetry index (AI) was calculated based on the following formula: AI=100×(rFC-lFC)/[(rFC+lFC)/2]${\rm{AI\ }} = {\rm{\ }}100{\rm{\ }} \times {\rm{\ }}( {{\rm{rFC}} - {\rm{lFC}}} )/[ {( {{\rm{rFC}} + {\rm{lFC}}} )/2} ]$ . Paired t-test and univariate analysis of variance were used to analyze FC asymmetry. Linear correlation analysis was performed between significant FC changes and lateralized ROIs and epilepsy onset age and duration.

RESULTS

LMTLE and RMTLE patients showed different patterns of alteration in FC and functional asymmetry when compared with controls. RMTLE presented more extensive FC abnormalities than LMTLE. Regions in ipsilateral temporal lobe presented as central regions of abnormalities in both patient groups. In addition, the asymmetric characteristics of FC were reduced in MTLE compared with HC, with even more pronounced reduction for RMTLE group. Meanwhile, ROIs presented FC AI differences among the three groups were mostly involving left temporal lobe (L_hippo, L_amyg, L_TP, L_aMTG, and L_pTFusC). No correlation was found between significant FC changes and lateralized ROIs and epilepsy onset age and duration.

CONCLUSION

The FC and asymmetric features of MTLE are altered and involve both the temporal lobe and extra-temporal lobe. Furthermore, the altered FC and asymmetric features were distinctly affected in LMTLE and RMTLE compared to controls.

Characterizing Brain Network Dynamics using Persistent Homology in Patients with Refractory Epilepsy

Epilepsy is a common serious neurological disorder that affects more than 65 million persons worldwide and it is characterized by repeated seizures that lead to higher mortality and disabilities with corresponding negative impact on the quality of life of patients. Network science methods that represent brain regions as nodes and the interactions between brain regions as edges have been extensively used in characterizing network changes in neurological disorders. However, the limited ability of graph network models to represent high dimensional brain interactions are being increasingly realized in the computational neuroscience community. In particular, recent advances in algebraic topology research have led to the development of a large number of applications in brain network studies using topological structures. In this paper, we build on a fundamental construct of cliques, which are all-to-all connected nodes with a k-clique in a graph G (V, E), where V is set of nodes and E is set of edges, consisting of k-nodes to characterize the brain network dynamics in epilepsy patients using topological structures. Cliques represent brain regions that are coupled for similar functions or engage in information exchange; therefore, cliques are suitable structures to characterize the dynamics of brain dynamics in neurological disorders. We propose to detect and use clique structures during well-defined clinical events, such as epileptic seizures, to combine non-linear correlation measures in a matrix with identification of geometric structures underlying brain connectivity networks to identify discriminating features that can be used for clinical decision making in epilepsy neurological disorder.

Characterization of resting functional MRI activity alterations across epileptic foci and networks

Brain network alterations have been studied extensively in patients with mesial temporal lobe epilepsy (mTLE) and other focal epilepsies using resting-state functional magnetic resonance imaging (fMRI). However, little has been done to characterize the basic fMRI signal alterations caused by focal epilepsy. Here, we characterize how mTLE affects the fMRI signal in epileptic foci and networks. Resting-state fMRI and diffusion MRI were collected from 47 unilateral mTLE patients and 96 healthy controls. FMRI activity, quantified by amplitude of low-frequency fluctuations, was increased in the epileptic focus and connected regions in mTLE. Evidence for spread of this epileptic fMRI activity was found through linear relationships of regional activity across subjects, the association of these relationships with functional connectivity, and increased activity along white matter tracts. These fMRI activity increases were found to be dependent on the epileptic focus, where the activity was related to disease severity, suggesting the focus to be the origin of these pathological alterations. Furthermore, we found fMRI activity decreases in the default mode network of right mTLE patients with different properties than the activity increases found in the epileptic focus. This work provides insights into basic fMRI signal alterations and their potential spread across networks in focal epilepsy.

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.

Temporal Lobe Spikes Affect Distant Intrinsic Connectivity Networks

Objective: To evaluate local and distant blood oxygen level dependent (BOLD) signal changes related to interictal epileptiform discharges (IED) in drug-resistant temporal lobe epilepsy (TLE).

Methods: Thirty-three TLE patients undergoing EEG–functional Magnetic Resonance Imaging (fMRI) as part of the presurgical workup were consecutively enrolled. First, a single-subject spike-related analysis was performed: (a) to verify the BOLD concordance with the presumed Epileptogenic Zone (EZ); and (b) to investigate the Intrinsic Connectivity Networks (ICN) involvement. Then, a group analysis was performed to search for common BOLD changes in TLE.

Results: Interictal epileptiform discharges were recorded in 25 patients and in 19 (58%), a BOLD response was obtained at the single-subject level. In 42% of the cases, BOLD changes were observed in the temporal lobe, although only one patient had a pure concordant finding, with a single fMRI cluster overlapping (and limited to) the EZ identified by anatomo-electro-clinical correlations. In the remaining 58% of the cases, BOLD responses were localized outside the temporal lobe and the presumed EZ. In every patient, with a spike-related fMRI map, at least one ICN appeared to be involved. Four main ICNs were preferentially involved, namely, motor, visual, auditory/motor speech, and the default mode network. At the single-subject level, EEG–fMRI proved to have high specificity (above 65%) in detecting engagement of an ICN and the corresponding ictal/postictal symptom, and good positive predictive value (above 67%) in all networks except the visual one. Finally, in the group analysis of BOLD changes related to IED revealed common activations at the right precentral gyrus, supplementary motor area, and middle cingulate gyrus.

Significance: Interictal temporal spikes affect several distant extra-temporal areas, and specifically the motor/premotor cortex. EEG–fMRI in patients with TLE eligible for surgery is recommended not for strictly localizing purposes rather it might be useful to investigate ICNs alterations at the single-subject level.

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.

Multitier Network Analysis Using Resting-State Functional MRI for Epilepsy Surgery

Resting-state functional MRI (rs-fMRI) has been utilized to visualize large-scale brain networks. We evaluated the usefulness of multitier network analysis using rs-fMRI in patients with focal epilepsy. Structural and rs-fMRI data were retrospectively evaluated in 20 cases with medically refractory focal epilepsy, who subsequently underwent surgery. First, structural changes were examined using voxel-based morphometry analysis. Second, alterations in large-scale networks were evaluated using dual-regression analysis. Third, changes in cortical hubs were analyzed and the relationship between aberrant hubs and the epileptogenic zone (EZ) was evaluated. Finally, the relationship between the hubs and the default mode network (DMN) was examined using spectral dynamic causal modeling (spDCM). Dual-regression analysis revealed significant decrease in functional connectivity in several networks including DMN in patients, although no structural difference was seen between groups. Aberrant cortical hubs were observed in and around the EZ (EZ hubs) in 85% of the patients, and a strong degree of EZ hubs correlated to good seizure outcomes postoperatively. In spDCM analysis, facilitation was often seen from the EZ hub to the contralateral side, while inhibition was seen from the EZ hub to nodes of the DMN. Some cognition-related networks were impaired in patients with focal epilepsy. The EZ hub appeared in the vicinity of EZ facilitating connections to distant regions in the early phase, which may eventually generate secondary focus, while inhibiting connections to the DMN, which may cause cognitive deterioration. Our results demonstrate pathological network alterations in epilepsy and suggest that earlier surgical intervention may be more effective.

Localizing the Epileptogenic Zone with Novel Biomarkers

Several noninvasive methods, such as high-density EEG or magnetoencephalography, are currently used to delineate the epileptogenic zone (EZ) during the presurgical evaluation of patients with drug resistant epilepsy (DRE). Yet, none of these methods can reliably identify the EZ by their own. In most cases a multimodal approach is needed. Challenging cases often require the implantation of intracranial electrodes, either through stereo-taxic EEG or electro-corticography. Recently, a growing body of literature introduces novel biomarkers of epilepsy that can be used for analyzing both invasive as well as noninvasive electrophysiological data. Some of these biomarkers are able to delineate the EZ with high precision, augment the presurgical evaluation, and predict the surgical outcome of patients with DRE undergoing surgery. However, the use of these epilepsy biomarkers in clinical practice is limited. Here, we summarize and discuss the latest technological advances in the presurgical neurophysiological evaluation of children with DRE with emphasis on electric and magnetic source imaging, high frequency oscillations, and functional connectivity.

Small loci of astroglial glutamine synthetase deficiency in the postnatal brain cause epileptic seizures and impaired functional connectivity

OBJECTIVE

The astroglial enzyme glutamine synthetase (GS) is deficient in small loci in the brain in adult patients with different types of focal epilepsy; however, the role of this deficiency in the pathogenesis of epilepsy has been difficult to assess due to a lack of sufficiently sensitive and specific animal models. The aim of this study was to develop an in vivo approach for precise and specific deletions of the GS gene in the postnatal brain.

METHODS

We stereotaxically injected various adeno-associated virus (AAV)-Cre recombinase constructs into the hippocampal formation and neocortex in 22-70-week-old GS^flox/flox mice to knock out the GS gene in a specific and focal manner. The mice were subjected to seizure threshold determination, continuous video-electroencephalographic recordings, advanced in vivo neuroimaging, and immunocytochemistry for GS.

RESULTS

The construct AAV8-glial fibrillary acidic protein-green fluorescent protein-Cre eliminated GS in >99% of astrocytes in the injection center with a gradual return to full GS expression toward the periphery. Such focal GS deletion reduced seizure threshold, caused spontaneous recurrent seizures, and diminished functional connectivity.

SIGNIFICANCE

These results suggest that small loci of GS deficiency in the postnatal brain are sufficient to cause epilepsy and impaired functional connectivity. Additionally, given the high specificity and precise spatial resolution of our GS knockdown approach, we anticipate that this model will be extremely useful for rigorous in vivo and ex vivo studies of astroglial GS function at the brain-region and single-cell levels.

Forced Normalization Revisited: New Concepts About a Paradoxical Phenomenon

The phenomenon of Forced Normalization (FN) was first described by Landolt in 1953, who described the disappearance of epileptiform discharges in the EEG of patients with epilepsy, concomitant with the development of psychotic symptoms. Later, Tellenbach coined the term “alternative psychosis” referring specifically to the alternation between clinical phenomena. Finally, in 1991, Wolf observed a degenerative process involved in the phenomenon, which he called “paradoxical normalization.” Initially, FN was explained through experimental models in animals and the demonstration of the kindling phenomenon, in its electrical and pharmacological subdivisions. At this stage of research on the epileptic phenomenon, repetitive electrical stimuli applied to susceptible regions of the brain (hippocampus and amygdala) were considered to explain the pathophysiological basis of temporal lobe epileptogenesis. Likewise, through pharmacological manipulation, especially of dopaminergic circuits, psychiatric comorbidities began to find their basic mechanisms. With the development of new imaging techniques (EEG/fMRI), studies in the area started to focus on the functional connectivity (FC) of different brain regions with specific neuronal networks, which govern emotions. Thus, a series of evidence was produced relating the occurrence of epileptic discharges in the limbic system and their consequent coactivation and deactivation of these resting-state networks. However, there are still many controversies regarding the basic mechanisms of network alterations related to emotional control, which will need to be studied with a more homogeneous methodology, in order to try to explain this interesting neuropsychiatric phenomenon with greater accuracy.

Hijacking of hippocampal-cortical oscillatory coupling during sleep in temporal lobe epilepsy

Memory impairment is the most common cognitive deficit in patients with temporal lobe epilepsy (TLE). This type of epilepsy is currently regarded as a network disease because of its brain-wide alterations in functional connectivity between temporal and extra-temporal regions. In patients with TLE, network dysfunctions can be observed during ictal states, but are also described interictally during rest or sleep. Here, we examined the available literature supporting the hypothesis that hippocampal-cortical coupling during sleep is hijacked in TLE. First, we look at studies showing that the coordination between hippocampal sharp-wave ripples (100-200 Hz), corticothalamic spindles (9-16 Hz), and cortical delta waves (1-4 Hz) during nonrapid eye movement (NREM) sleep is critical for spatial memory consolidation. Then, we reviewed studies showing that animal models of TLE display precise coordination between hippocampal interictal epileptiform discharges (IEDs) and spindle oscillations in the prefrontal cortex. This aberrant oscillatory coupling seems to surpass the physiological ripple-delta-spindle coordination, which could underlie memory consolidation impairments. We also discuss the role of rapid eye movement (REM) sleep for local synaptic plasticity and memory. Sleep episodes of REM provide windows of opportunity for reactivation of expression of immediate early genes (i.e., zif-268 and Arc). Besides, hippocampal theta oscillations during REM sleep seem to be critical for memory consolidation of novel object place recognition task. However, it is still unclear which extend this particular phase of sleep is affected in TLE. In this context, we show some preliminary results from our group, suggesting that hippocampal theta-gamma phase-amplitude coupling is exacerbated during REM in a model of basolateral amygdala fast kindling. In conclusion, there is an increasing body of evidence suggesting that circuits responsible for memory consolidation during sleep seem to be gradually coopted and degraded in TLE. This article is part of the Special Issue "NEWroscience 2018".

Highly consistent temporal lobe interictal spike networks revealed from foramen ovale electrodes

OBJECTIVE

A major challenge that limits understanding and treatment of epileptic events from mesial temporal structures comes from our inability to detect and map interictal networks reproducibly using scalp electrodes. Here, we developed a novel approach to map interictal spike networks and demonstrate their relationships to seizure onset and lesions in patients with foramen ovale electrode implantations.

METHODS

We applied the direct Directed Transfer Function to reveal interictal spike propagation from bilateral foramen ovale electrodes on 10 consecutive patients and co-registered spatially with both seizure onset zones and temporal lobe lesions.

RESULTS

Highly reproducible, yet unique interictal spike networks were seen for each patient (correlation: 0.93 ± 0.13). Interictal spikes spread in both anterior and posterior directions within each temporal lobe, often reverberating between sites. Spikes propagated to the opposite temporal lobe predominantly through posterior pathways. Patients with structural lesions (N = 4), including tumors and sclerosis, developed reproducible spike networks adjacent to their lesions that were highly lateralized compared to patients without lesions. Only 5% of mesial temporal lobe spikes were time-locked with scalp electrode spikes. Our preliminary observation on two lesional patients suggested that along with lesion location, Interictal spike networks also partially co-registered with seizure onset zones suggesting interrelationship between seizure onset and a subset of spike networks.

CONCLUSIONS

This is the first demonstration of patient-specific, reproducible interictal spike networks in mesial temporal structures that are closely linked to both temporal lobe lesions and seizure onset zones.

SIGNIFICANCE

Interictal spike connectivity is a novel approach to map epileptic networks that could help advance invasive and non-invasive epilepsy treatments.

Evaluation of Brain Network Properties in Patients with MRI-Negative Temporal Lobe Epilepsy: An MEG Study

Abnormal functional brain networks of temporal lobe epilepsy (TLE) patients with structural abnormalities may partially reflect structural lesions rather than either TLE per se or functional compensatory processes. In this study, we sought to investigate the brain-network properties of intractable TLE patients apart from the effects of structural abnormalities. The brain network properties of 20 left and 23 right MRI-negative TLE patients and 22 healthy controls were evaluated using magnetoencephalographic recordings in six main frequency bands. A slowing of oscillatory brain activity was observed for the left or right TLE group vs. healthy controls. The TLE groups presented significantly increased functional connectivity in the delta, theta, lower alpha and beta bands, and significantly greater values in the normalized clustering coefficient and path length, and significantly smaller values in the weighted small-world measure in the theta band when compared to healthy controls. Alterations in global and regional band powers can be attributed to spectral slowing in TLE patients. The brain networks of TLE patients displayed abnormally high synchronization in multi-frequency bands and shifted toward a more regular architecture with worse network efficiency in the theta band. Without the contamination of structural lesions, these significant findings can be helpful for better understanding of the pathophysiological mechanism of TLE. The theta band can be considered as a preferred frequency band for investigating the brain-network dysfunction of MRI-negative intractable TLE patients.