Evolution of functional connectivity of brain networks and their dynamic interaction in temporal lobe epilepsy

Decomposed FDG PET-based phenotypic heterogeneity predicting clinical prognosis and decision-making in temporal lobe epilepsy patients

OBJECTIVE

This study utilized a data-driven Bayesian model to automatically identify distinct latent disease factors represented by overlapping glucose metabolism patterns from 18F-Fluorodeoxyglucose PET (18F-FDG PET) to analyze heterogeneity among patients with TLE.

METHODS

We employed unsupervised machine learning to estimate latent disease factors from 18F-FDG PET scans, representing whole-brain glucose metabolism patterns in seventy patients with TLE. We estimated the extent to which multiple distinct factors were expressed within each participant and analyzed their relevance to epilepsy burden, including seizure onset, duration, and frequency. Additionally, we established a predictive model for clinical prognosis and decision-making.

RESULTS

We identified three latent disease factors: hypometabolism in the unilateral temporal lobe and hippocampus (factor 1), hypometabolism in bilateral prefrontal lobes (factor 2), and hypometabolism in bilateral temporal lobes (factor 3), variably co-expressed within each patient. Factor 3 demonstrated the strongest negative correlation with the age of onset and duration (r =  - 0.33, - 0.38 respectively, P < 0.05). The supervised classifier, trained on latent disease factors for predicting patient-specific antiepileptic drug (AED) responses, achieved an area under the curve (AUC) of 0.655. For post-surgical seizure outcomes, the AUC was 0.857, and for clinical decision-making, it was 0.965.

CONCLUSIONS

Decomposing 18F-FDG PET-based phenotypic heterogeneity facilitates individual-level predictions relevant to disease monitoring and personalized therapeutic strategies.

Crosstalk between the subiculum and sleep-wake regulation: A review

The circuitry underlying the initiation, maintenance, and coordination of wakefulness, rapid eye movement sleep, and non-rapid eye movement sleep is not thoroughly understood. Sleep is thought to arise due to decreased activity in the ascending reticular arousal system, which originates in the brainstem and awakens the thalamus and cortex during wakefulness. Despite the conventional association of sleep-wake states with hippocampal rhythms, the mutual influence of the hippocampal formation in regulating vigilance states has been largely neglected. Here, we focus on the subiculum, the main output region of the hippocampal formation. The subiculum, particulary the ventral part, sends extensive monosynaptic projections to crucial regions implicated in sleep-wake regulation, including the thalamus, lateral hypothalamus, tuberomammillary nucleus, basal forebrain, ventrolateral preoptic nucleus, ventrolateral tegmental area, and suprachiasmatic nucleus. Additionally, second-order projections from the subiculum are received by the laterodorsal tegmental nucleus, locus coeruleus, and median raphe nucleus, suggesting the potential involvement of the subiculum in the regulation of the sleep-wake cycle. We also discuss alterations in the subiculum observed in individuals with sleep disorders and in sleep-deprived mice, underscoring the significance of investigating neuronal communication between the subiculum and pathways promoting both sleep and wakefulness.

A scoping review of the functional magnetic resonance imaging-based functional connectivity of focal cortical dysplasia-related epilepsy

Focal cortical dysplasia (FCD) is the most frequent etiology of operable pharmacoresistant epilepsy in children. There is burgeoning evidence that FCD-related epilepsy is a disorder that involves distributed brain networks. Functional magnetic resonance imaging (fMRI) is a tool that allows one to infer neuronal activity and to noninvasively map whole-brain functional networks. Despite its relatively widespread availability at most epilepsy centers, the clinical application of fMRI remains mostly task-based in epilepsy. Another approach is to map and characterize cortical functional networks of individuals using resting state fMRI (rsfMRI). The focus of this scoping review is to summarize the evidence to date of investigations of the network basis of FCD-related epilepsy, and to highlight numerous potential future applications of rsfMRI in the exploration of diagnostic and therapeutic strategies for FCD-related epilepsy. There are numerous studies demonstrating a global disruption of cortical functional networks in FCD-related epilepsy. The underlying pathological subtypes of FCD influence overall functional network patterns. There is evidence that cortical functional network mapping may help to predict postsurgical seizure outcomes, highlighting the translational potential of these findings. Additionally, several studies emphasize the important effect of FCD interaction with cortical networks and the expression of epilepsy and its comorbidities.

T1-/T2-weighted ratio reveals no alterations to gray matter myelination in temporal lobe epilepsy

Short-range functional connectivity in the limbic network is increased in patients with temporal lobe epilepsy (TLE), and recent studies have shown that cortical myelin content correlates with fMRI connectivity. We thus hypothesized that myelin may increase progressively in the epileptic network. We compared T1w/T2w gray matter myelin maps between TLE patients and age-matched controls and assessed relationships between myelin and aging. While both TLE patients and healthy controls exhibited increased T1w/T2w intensity with age, we found no evidence for significant group-level aberrations in overall myelin content or myelin changes through time in TLE.

Intracortical functional connectivity dynamics induced by reflex seizures

Functional connectivity analysis is gaining more interest due to its promising clinical applications. To study network mechanisms underlying seizure termination and postictal depression, we explore dynamics of interhemispheric functional connectivity near the offset of focal and bilateral seizures in the experimental model of reflex audiogenic epilepsy. In the model, seizures and spreading depression are induced by sound stimulation of genetically predisposed rodents. We characterize temporal evolution of seizure-associated coupling dynamics in the frontoparietal cortex during late ictal, immediate postictal and interictal resting states, using two measures applied to local field potentials recorded in awake epileptic rats. Signals were analyzed with mean phase coherence index in delta (1-4 Hz), theta (4-10 Hz) beta (10-25 Hz) and gamma (25-50 Hz) frequency bands and mutual information function. The study shows that reflex seizures elicit highly dynamic changes in interhemispheric functional coupling with seizure-, region- and frequency-specific patterns of increased and decreased connectivity during late ictal and immediate postictal periods. Also, secondary generalization of recurrent seizures (kindling) is associated with pronounced alterations in resting-state functional connectivity - an early wideband decrease and a subsequent beta-gamma increase. The findings show that intracortical functional connectivity is dynamically modified in response to seizures on short and long timescales, suggesting the existence of activity-dependent plastic network alterations that may promote or prevent seizure propagation within the cortex and underlie postictal behavioral impairments.

Long-term ANT-DBS effects in pilocarpine-induced epileptic rats: A combined 9.4T MRI and histological study

Deep brain stimulation (DBS) of the anterior nucleus of the thalamus (ANT) appears to be effective against seizures in animals and humans however, its therapeutic mechanisms remain elusive. This study aimed to combine 9.4T multimodal magnetic resonance imaging (MRI) with histology to investigate the longitudinal effects of long-term ANT-DBS in pilocarpine-induced epileptic rats. Status epilepsy (SE) was induced by LiCl-pilocarpine injection in 11 adult male Sprague-Dawley rats. Four weeks after SE, chronic epileptic rats underwent either ANT-DBS (n = 6) or sham-DBS (n = 5) surgery. Electroencephalography (EEG) and spontaneous recurrent seizures (SRS) were recorded for 1 week. The T2-weighted image and images from resting-state functional MRI (rs-fMRI) were acquired at three states: before SE, at 4 weeks post-SE, and at 5 weeks post-DBS. Volumes of the hippocampal subregions and hippocampal-related functional connectivity (FC) were compared longitudinally. Finally, antibodies against neuronal nuclei (NeuN) and glial fibrillary acidic proteins were used to evaluate neuronal loss and astrogliosis in the hippocampus. Long-term ANT-DBS significantly reduced seizure generalization in pilocarpine-induced epileptic rats. By analyzing the gray matter volume using T2-weighted images, long-term ANT-DBS displayed morphometric restoration of the hippocampal subregions. Neuronal protection of the hippocampal subregions and inhibition of astrogliosis in the hippocampal subregions were observed in the ANT-DBS group. ANT-DBS caused reversible regulation of FC in the insula-hippocampus and subthalamic nucleus-hippocampus. Long-term ANT-DBS provides comprehensive protection of hippocampal histology, hippocampal morphometrics, and hippocampal-related functional networks.

Volume of hippocampal activation as a determining factor for the lateralisation of the epileptogenic zone in patients with drug-resistant mesial temporal lobe epilepsy

INTRODUCTION

Approximately 30% of patients with mesial temporal lobe epilepsy (MTLE) will develop drug resistance, which requires early surgical treatment. The success of the surgical procedure largely depends on the correct lateralisation of the epileptogenic zone, which can only be determined in 70% of patients with such conventional diagnostic tools as video electroencephalography and volumetric structural magnetic resonance imaging. We evaluated the performance of a memory functional magnetic resonance imaging (fMRI) paradigm in lateralising the epileptogenic zone in patients with drug-resistant MTLE.

METHODS

We included 18 patients with MTLE attended at the Instituto Neurológico Colombiano in Medellin (Colombia) between 2018 and 2019. The volume of functional activation in both temporal lobes was determined with a memory fMRI paradigm. A concordance analysis was performed to compare the performance of fMRI against that of conventional tests.

RESULTS

In patients with left MTLE, lower total activation was found in the hemisphere ipsilateral to the epileptogenic zone as compared to the contralateral hemisphere (121.15 ± 16.48 voxels vs 170.23 ± 17.8 voxels [P < .001]), showing substantial concordance with conventional tests. Patients with right MTLE displayed lower hippocampal activation ipsilateral to the epileptogenic zone (18.5 ± 3.38 voxels vs 27.8 ± 3.77 voxels in the contralateral hippocampus [P = .048]), showing moderate concordance with conventional tests.

CONCLUSIONS

These findings suggest that lower functional activation as determined by a memory fMRI paradigm has a high level of concordance with conventional tests for lateralising the epileptogenic zone in patients with drug-resistant MTLE.

A computational model based on corticospinal functional MRI revealed asymmetrically organized motor corticospinal networks in humans

Evolution of the direct, monosynaptic connection from the primary motor cortex to the spinal cord parallels acquisition of hand dexterity and lateralization of hand preference. In non-human mammals, the indirect, multi-synaptic connections between the bilateral primary motor cortices and the spinal cord also participates in controlling dexterous hand movement. However, it remains unknown how the direct and indirect corticospinal pathways work in concert to control unilateral hand movement with lateralized preference in humans. Here we demonstrated the asymmetric functional organization of the two corticospinal networks, by combining network modelling and simultaneous functional magnetic resonance imaging techniques of the brain and the spinal cord. Moreover, we also found that the degree of the involvement of the two corticospinal networks paralleled lateralization of hand preference. The present results pointed to the functionally lateralized motor nervous system that underlies the behavioral asymmetry of handedness in humans.

MRI and network modelling reveal correlation between the degree of involvement of the two corticospinal networks and the lateralization of handedness in humans.

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

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSION

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

SIGNIFICANCE

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

Episodic Memory Dysfunction and Effective Connectivity in Adult Patients With Newly Diagnosed Nonlesional Temporal Lobe Epilepsy

Objective

Epilepsy is associated with both changes in brain connectivity and memory function, usually studied in the chronic patients. The aim of this study was to explore the presence of connectivity alterations measured by EEG in the parietofrontal network in patients with temporal lobe epilepsy (TLE), and to examine episodic memory, at the time point of diagnosis.

Methods

The parietofrontal network of newly diagnosed patients with TLE (N = 21) was assessed through electroencephalography (EEG) effective connectivity and compared with that of matched controls (N = 21). Furthermore, we assessed phenomenological aspects of episodic memory in both groups. Association between effective connectivity and episodic memory were assessed through correlation.

Results

Patients with TLE displayed decreased episodic (p ≤ 0.001, t = −5.18) memory scores compared with controls at the time point of diagnosis. The patients showed a decreased right parietofrontal connectivity (p = 0.03, F = 4.94) compared with controls, and significantly weaker connectivity in their right compared with their left hemisphere (p = 0.008, t = −2.93). There were no significant associations between effective connectivity and episodic memory scores.

Conclusions

We found changes in both memory function and connectivity at the time point of diagnosis, supporting the notion that TLE involves complex memory functions and brain networks beyond the seizure focus to strongly interconnected brain regions, already early in the disease course. Whether the observed connectivity changes can be interpreted as functionally important to the alterations in memory function, it remains speculative.

Individual [18F]FDG PET and functional MRI based on simultaneous PET/MRI may predict seizure recurrence after temporal lobe epilepsy surgery

OBJECTIVES

To investigate the individual measures of brain glucose metabolism, neural activity obtained from simultaneous ¹⁸[F]FDG PET/MRI, and their association with surgical outcomes in medial temporal lobe epilepsy due to hippocampal sclerosis (mTLE-HS).

METHODS

Thirty-nine unilateral mTLE-HS patients who underwent anterior temporal lobectomy were classified as having completely seizure-free (Engel class IA; n = 22) or non-seizure-free (Engel class IB-IV; n = 17) outcomes at 1 year after surgery. Preoperative [¹⁸F]FDG PET and functional MRI (fMRI) were obtained from a simultaneous PET/MRI scanner, and individual glucose metabolism and fractional amplitude of low-frequency fluctuation (fALFF) were evaluated by standardizing these with respect to healthy controls. These abnormality measures and clinical data from each patient were incorporated into a machine learning framework (gradient boosting decision tree and logistic regression analysis) to estimate seizure recurrence. The predictive values of features were evaluated by the receiver operating characteristic (ROC) curve in the training and test cohorts.

RESULTS

The machine learning classification model showed [¹⁸F]FDG PET and fMRI variations in contralateral hippocampal network and age of onset identify unfavorable surgical outcomes effectively. In the validation dataset, the logistic regression model with [¹⁸F]FDG PET and fALFF obtained from simultaneous [¹⁸F]FDG PET/MRI gained the maximum area under the ROC curve of 0.905 for seizure recurrence, higher than 0.762 with ¹⁸[F]-FDG PET, and 0.810 with fALFF alone.

CONCLUSION

Machine learning model suggests individual [¹⁸F]FDG PET and fMRI variations in contralateral hippocampal network based on ¹⁸[F]-FDG PET/MRI could serve as a potential biomarker of unfavorable surgical outcomes.

KEY POINTS

• Individual [¹⁸F]FDG PET and fMRI obtained from preoperative [¹⁸F]FDG PET/MR were investigated. • Individual differences were further assessed based on a seizure propagation network. • Machine learning can classify surgical outcomes with 90.5% accuracy.

Association Between Anatomical Location of Surgically Induced Lesions and Postoperative Seizure Outcome in Temporal Lobe Epilepsy

BACKGROUND AND OBJECTIVES

To determine the association between surgical lesions of distinct gray and white structures and connections with favorable postoperative seizure outcomes.

METHODS

Patients with drug-resistant temporal lobe epilepsy (TLE) from 3 epilepsy centers were included. We employed a voxel-based and connectome-based mapping approach to determine the association between favorable outcomes and surgery-induced temporal lesions. Analyses were conducted controlling for multiple confounders, including total surgical resection/ablation volume, hippocampal volumes, side of surgery, and site where the patient was treated.

RESULTS

The cohort included 113 patients with TLE (54 women; 86 right-handed; mean age at seizure onset 16.5 years [SD 11.9]; 54.9% left) who were 61.1% free of disabling seizures (Engel Class 1) at follow-up. Postoperative seizure freedom in TLE was associated with (1) surgical lesions that targeted the hippocampus as well as the amygdala-piriform cortex complex and entorhinal cortices; (2) disconnection of temporal, frontal, and limbic regions through loss of white matter tracts within the uncinate fasciculus, anterior commissure, and fornix; and (3) functional disconnection of the frontal (superior and middle frontal gyri, orbitofrontal region) and temporal (superior and middle pole) lobes.

DISCUSSION

Better postoperative seizure freedom is associated with surgical lesions of specific structures and connections throughout the temporal lobes. These findings shed light on the key components of epileptogenic networks in TLE and constitute a promising source of new evidence for future improvements in surgical interventions.

CLASSIFICATION OF EVIDENCE

This study provides Class II evidence that for patients with TLE, postoperative seizure freedom is associated with surgical lesions of specific temporal lobe structures and connections.

Decreased Intrinsic Neural Timescales in Mesial Temporal Lobe Epilepsy

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

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.

Disrupted functional connectivity in white matter resting-state networks in unilateral temporal lobe epilepsy

Unilateral temporal lobe epilepsy (TLE) is the most common type of focal epilepsy characterized by foci in the unilateral temporal lobe grey matters of regions such as the hippocampus. However, it remains unclear how the functional features of white matter are altered in TLE. In the current study, resting-state functional magnetic resonance imaging (fMRI) was performed on 71 left TLE (LTLE) patients, 79 right TLE (RTLE) patients and 47 healthy controls (HC). Clustering analysis was used to identify fourteen white matter networks (WMN). The functional connectivity (FC) was calculated among WMNs and between WMNs and grey matter. Furthermore, the FC laterality of hemispheric WMNs was assessed. First, both patient groups showed decreased FCs among WMNs. Specifically, cerebellar white matter illustrated decreased FCs with the cerebral superficial WMNs, implying a dysfunctional interaction between the cerebellum and the cerebral cortex in TLE. Second, the FCs between WMNs and the ipsilateral hippocampus (grey matter foci) were also reduced in patient groups, which may suggest insufficient functional integration in unilateral TLE. Interestingly, RTLE showed more severe abnormalities of white matter FCs, including links to the bilateral hippocampi and temporal white matter, than LTLE. Taken together, these findings provide functional evidence of white matter abnormalities, extending the understanding of the pathological mechanism of white matter impairments in unilateral TLE.

Investigation of dynamic functional connectivity of the source reconstructed epileptiform discharges in focal epilepsy: A graph theory approach

OBJECTIVE

The aim of the present study is to investigate with noninvasive methods the modulation of dynamic functional connectivity during interictal epileptiform discharge (IED).

METHOD

We reconstructed the cortical source of the EEG recorded IED of 17 patients with focal epilepsy. We then computed dynamic connectivity using the time resolved phase locking value (PLV). We derived graph theory indices (i.e. degree, strength, local efficiency, clustering coefficient and global efficiency). Finally, we selected the atlas node with the maximum activation as the IED cortical source investigating the graph indices dynamics in theta, alpha, beta and gamma frequency bands.

RESULTS

We observed IED-locked modulations of the graph indexes depending on the frequency bands. We detected a modulation of the strength, clustering coefficient, local and global efficiency both in theta and in alpha bands, which also displayed modulations of the degree index. In the beta band only the global efficiency was modulated by the IED, while no effects were detected in the gamma band. Finally, we found a correlation between alpha and theta local efficiency, as well as alpha global efficiency, and the epilepsy duration.

SIGNIFICANCE

Our findings suggest that the neural synchronization is not limited to the IED cortical source, but implies a phase synchronization across multiple brain areas. We hypothesize that the aberrant electrical activity originating from the IED locus is spread amongst the other network nodes throughout the low frequency bands (i.e. theta and alpha). Moreover, IED-dependent increase in the global efficiency indicates that the IED interfere with the whole network functioning. We finally discussed possible application of this methodology for future investigation.

Exploring factors associated with interictal heart rate variability in patients with medically controlled focal epilepsy

PURPOSE

Heart rate variability (HRV) reflects the balance between the functional outputs of the sympathetic and parasympathetic nervous systems. It is lower in patients with epilepsy than in the healthy controls. However, HRV has been inadequately studied in different patient subgroups with medically controlled epilepsy. Hence, this study aimed to investigate factors associated with interictal HRV in patients with medically controlled epilepsy.

METHODS

This retrospective cohort study included 54 patients (24 males and 30 females) with medically controlled focal epilepsy who only received monotherapy to eliminate the confounding effect of different antiseizure medications (ASMs). Patients with major systemic or psychiatric disorder comorbidities were excluded. For HRV analysis, electroencephalography and 5-minute well-qualified electrocardiogram segment recording were conducted during stage N1 or N2 sleep. In addition, the association between age, gender, seizure onset type, ASMs, and the time domain and frequency-domain HRV measures was analyzed.

RESULTS

HRV negatively correlated with advanced age. Patients with focal to bilateral tonic-clonic seizure (FBTCS) had a significantly lower HRV than focal impaired awareness seizures (FIAS). HRV was not associated with any gender and ASMs.

CONCLUSIONS

HRV negatively correlated with age, and patients with FBTCS had a decreased HRV. Thus, these patients may have a declining autonomic function. Therefore, different seizure types may carry different risks of autonomic dysfunction in patients with medically controlled focal epilepsy.

Experience From 211 Transcortical Selective Amygdalohippocampectomy Procedures: Relevant Surgical Anatomy and Review of the Literature

BACKGROUND

Selective amygdalohippocampectomy (SelAH) is designed to treat medically refractory mesial temporal lobe epilepsy with reduced morbidity compared to standard anterior temporal lobectomy. At our institution, we perform SelAH via a transcortical approach via small corticectomy in the middle temporal gyrus.

OBJECTIVE

To discuss the surgical anatomy and nuances of SelAH, share our institutional experience, and perform a review of literature.

METHODS

Institutional experience was recorded by collecting demographic and outcome data from 1999 to 2017 under an Institutional Review Board protocol in a prospective manner using a REDCap database.

RESULTS

A total of 211 SelAH procedures were performed at our institution between 1999 and 2017. Of these patients, 54% (113/211) were females. The average age at surgery was 39.4 yr. Two-year Engel outcome data were available for 168 patients, of which 73% (123/168) had Engel I outcomes. Engel II outcomes were reported in 16.6% (28/168), III in 4.7% (8/168), and IV in 5.3% (9/168). Our review of literature showed that this is comparable to the seizure freedom rates reported by other groups. We then reviewed our surgical methodology based on operative reports and created illustrations of the surgical anatomy of temporal lobe approach. These illustrations were compared with postoperative magnetic resonance imaging to provide a better 3D understanding of the complex architecture of mesial temporal structures.

CONCLUSION

SelAH is a minimally invasive, safe, and effective approach for the treatment of medically refractory epilepsy with good surgical outcomes and low morbidity. We feel that mastering the complex anatomy of this approach helps achieve successful outcomes.

Network reorganization during verbal fluency task in fronto-temporal epilepsy: A functional near-infrared spectroscopy study

This is the first study to use functional near-infrared spectroscopy (fNIRS) to investigate how the lateralization of the epileptogenic zone affects the reconfiguration of task-related network patterns. Eleven left fronto-temporal epilepsy (L-FTE) and 11 right fronto-temporal epilepsy (R-FTE), as well as 22 age- and gender-matched controls, were enrolled. Signals from 52-channel fNIRS were recorded while the subject was undertaking verbal fluency tasks (VFTs), which included categorical (CFT) and letter (LFT) fluency tasks. Three analytic methods were used to study the network topology: network-based analysis, hub identification, and proportional threshold to select the top 20% strongest connections for both graph theory parameters and clinical correlation. Performance of CFT is accomplished primarily using the ventral pathway, and bilateral ventral pathways are augmented in fronto-temporal epilepsy patients by strengthening the inter-hemispheric connections, especially for R-FTE. LFT mainly employed the dorsal pathway, and further prioritized the left dorsal pathway in strengthening intra-hemispheric connections in fronto-temporal epilepsy, especially L-FTE. The top 20% of the strongest connections only present differences in CFT network compared with the controls. R-FTE increased inter-hemispheric network density, while L-FTE decreased inter-hemispheric average characteristic path length. Accumulative seizure burden only affects L-FTE network. Better LFT performance and longer educational years seem to promote left fronto-temporal networks, and decreased the demand from RR intra-hemispheric connectivity in L-FTE. LFT scores in R-FTE are maintained by preserved RR intra-hemispheric networks. However, CFT scores and educational years seem to have no effect on the CFT network topology in both FTE.

Temporal instability of salience network activity in migraine with aura

This study aims to investigate whether intranetwork dynamic functional connectivity and causal interactions of the salience network is altered in the interictal term of migraine. Thirty-two healthy controls, 37 migraineurs without aura, and 20 migraineurs with aura were recruited. Participants underwent a T1-weighted scan and resting-state fMRI protocol inside a 1.5T MR scanner. We obtained average spatial maps of resting-state networks using group independent component analysis, which yielded subject-specific time series through a dual regression approach. Salience network regions of interest (bilateral insulae and prefrontal cortices, dorsal anterior cingulate cortex) were obtained from the group average map through cluster-based thresholding. To describe intranetwork connectivity, average and dynamic conditional correlation was calculated. Causal interactions between the default-mode, dorsal attention, and salience network were characterised by spectral Granger's causality. Time-averaged correlation was lower between the right insula and prefrontal cortex in migraine without aura vs with aura and healthy controls (P < 0.038, P < 0.037). Variance of dynamic conditional correlation was higher in migraine with aura vs healthy controls and migraine with aura vs without aura between the right insula and dorsal anterior cingulate cortex (P < 0.011, P < 0.026), and in migraine with aura vs healthy controls between the dorsal anterior cingulate and left prefrontal cortex (P < 0.021). Causality was weaker in the <0.05 Hz frequency range between the salience and dorsal attention networks in migraine with aura (P < 0.032). Overall, migraineurs with aura exhibit more fluctuating connections in the salience network, which also affect network interactions, and could be connected to altered cortical excitability and increased sensory gain.

Regional and global resting-state functional MR connectivity in temporal lobe epilepsy: Results from the Epilepsy Connectome Project

Temporal lobe epilepsy (TLE) has been conceptualized as focal disease with a discrete neurobiological focus and can respond well to targeted resection or ablation. In contrast, the neuro-cognitive deficits resulting from TLE can be widespread involving regions beyond the primary epileptic network. We hypothesize that this seemingly paradoxical findings can be explained by differences in connectivity between the primary epileptic region which is hyper-connected and its secondary influence on global connectome organization. This hypothesis is tested using regional and global graph theory metrics where we anticipate that regional mesial-temporal hyperconnectivity will be found and correlate with seizure frequency while global networks will be disorganized and be more closely associated with neuro-cognitive deficits. Resting-state fMRI was used to examine temporal lobe regional connectivity and global functional connectivity from 102 patients with TLE and 55 controls. Connectivity matrices were calculated for subcortical volumes and cortical parcellations. Graph theory metrics (global clustering coefficient (GCC), degree, closeness) were compared between groups and in relation to neuropsychological profiles and disease covariates using permutation testing and causal analysis. In TLE there was a decrease in GCC (p = 0.0345) associated with a worse neuropsychological profile (p = 0.0134). There was increased connectivity in the left hippocampus/amygdala (degree p = 0.0103, closeness p = 0.0104) and a decrease in connectivity in the right lateral temporal lobe (degree p = 0.0186, closeness p = 0.0122). A ratio between the hippocampus/amygdala and lateral temporal lobe-temporal lobe connectivity ratio (TLCR) revealed differences between TLE and controls for closeness (left p = 0.00149, right p = 0.0494) and for degree on left p = 0.00169; with trend on right p = 0.0567. Causal analysis suggested that "Epilepsy Activity" (seizure frequency, anti-seizure medications) was associated with increase in TLCR but not in GCC, while cognitive decline was associated with decreased GCC. These findings support the hypothesis that in TLE there is hyperconnectivity in the hippocampus/amygdala and hypoconnectivity in the lateral temporal lobe associated with "Epilepsy Activity." While, global connectome disorganization was associated with worse neuropsychological phenotype.

Recent Advances in Neuroimaging of Epilepsy

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

Dynamic network connectivity predicts subjective cognitive decline: the Sino-Longitudinal Cognitive impairment and dementia study

Subjective cognitive decline (SCD) is the preclinical stage of Alzheimer's disease (AD), the most common neurodegenerative disease in the elderly. We collected resting-state functional MRI data and applied novel graph-theoretical analyses to investigate the dynamic spatiotemporal cerebral connectivities in 63 individuals with SCD and 67 normal controls (NC). Temporal flexibility and spatiotemporal diversity were mapped to reflect dynamic time-varying functional interactions among the brain regions within and outside communities. Temporal flexibility indicates how frequently a brain region interacts with regions of other communities across time; spatiotemporal diversity describes how evenly a brain region interacts with regions belonging to other communities. SCD and NC differed in large-scale brain dynamics characterized by the two measures, which, with support vector machine, demonstrated higher classification accuracies than conventional static parameters and structural metrics. The findings characterize dynamic network dysfunction that may serve as a biomarker of the preclinical stage of AD.

Cognitive task-related functional connectivity alterations in temporal lobe epilepsy

OBJECTIVE

We investigated cognitive task-related functional connectivity (FC) in patients with temporal lobe epilepsy (TLE). Using a visual three-stimulus paradigm (VTSP), we studied cognitive large-scale networks and the impact of TLE on connectivity outside the temporal lobe.

METHODS

High-density electroencephalography (EEG) was recorded during the paradigm from nineteen patients with epilepsy with hippocampal sclerosis (HS) and ten healthy controls (HCs). Scalp data were reconstructed into the source space, and FC was computed. Correlating with the neuropsychological data, possible compensatory mechanisms were investigated.

RESULTS

Significant changes were found in the FC of regions outside the epileptogenic network, particularly in the attentional network. These changes were more widespread in left TLE (LTLE). There were no significant differences in task performance (accuracy, time response) in comparison with HCs, implying that there must be some mechanism reducing the impact of connectivity changes on brain functions. When correlated with neuropsychological data, we found stronger compensatory mechanisms in right TLE (RTLE).

SIGNIFICANCE

Our findings confirm the hypothesis that LTLE is the more pervasive form of the disease. Even though the network alterations in TLE are severe, some mechanisms reduce the impact of epilepsy on cognitive functions; these mechanisms are more potent in RTLE. We also suggest that there are maladaptive mechanisms in LTLE.

Dynamic functional connectivity in temporal lobe epilepsy: a graph theoretical and machine learning approach

PURPOSE

Functional magnetic resonance imaging (fMRI) in resting state can be used to evaluate the functional organization of the human brain in the absence of any task or stimulus. The functional connectivity (FC) has non-stationary nature and consented to be varying over time. By considering the dynamic characteristics of the FC and using graph theoretical analysis and a machine learning approach, we aim to identify the laterality in cases of temporal lobe epilepsy (TLE).

METHODS

Six global graph measures are extracted from static and dynamic functional connectivity matrices using fMRI data of 35 unilateral TLE subjects. Alterations in the time trend of the graph measures are quantified. The random forest (RF) method is used for the determination of feature importance and selection of dynamic graph features including mean, variance, skewness, kurtosis, and Shannon entropy. The selected features are used in the support vector machine (SVM) classifier to identify the left and right epileptogenic sides in patients with TLE.

RESULTS

Our results for the performance of SVM demonstrate that the utility of dynamic features improves the classification outcome in terms of accuracy (88.5% for dynamic features compared with 82% for static features). Selecting the best dynamic features also elevates the accuracy to 91.5%.

CONCLUSION

Accounting for the non-stationary characteristics of functional connectivity, dynamic connectivity analysis of graph measures along with machine learning approach can identify the temporal trend of some specific network features. These network features may be used as potential imaging markers in determining the epileptogenic hemisphere in patients with TLE.

Cortical excitability in epilepsy and the impact of antiepileptic drugs: transcranial magnetic stimulation applications

INTRODUCTION

Epileptic conditions are characterized by impaired cortical excitation/inhibition balance and interneuronal disinhibition. Transcranial magnetic stimulation (TMS) is a neurophysiological method that assesses brain excitation/inhibition.

AREA COVERED

This review was written after a detailed search in PubMed, EMBASE, ISI web of science, SciELO, Scopus, and Cochrane Controlled Trials databases from 1990 to 2020. It summarizes TMS applications for diagnostic and therapeutic purposes in epilepsy. TMS studies help to distinguish different epilepsy conditions and explore the antiepileptic drugs' (AEDs') effects on neuronal microcircuits and plasticity mechanisms. Repetitive TMS studies showed that low-frequency rTMS (0.33-1 Hz) can reduce seizures' frequency in refractory epilepsy or pause ongoing seizures; however, there is no current approval for its use in such patients as adjunctive treatment to AEDs.

EXPERT OPINION

There are variable and conflicting TMS results which reflect the distinct pathogenic mechanisms of each epilepsy condition, the dynamic epileptogenic process over the long disease course resulting in the development of recurrent spontaneous seizures and/or progression of epilepsy after it is established, and the differential effect of AEDs on cortical excitability. Future epilepsy research should focus on combined TMS/functional connectivity studies that explore the complex cortical excitability circuits and networks using different TMS parameters and techniques.

Functional Networks in Epilepsy Presurgical Evaluation

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

Multidimensional associations between cognition and connectome organization in temporal lobe epilepsy

OBJECTIVE

Temporal lobe epilepsy (TLE) is known to affect large-scale structural networks and cognitive function in multiple domains. The study of complex relations between structural network organization and cognition requires comprehensive analytical methods and a shift towards multivariate techniques. Here, we sought to identify multidimensional associations between cognitive performance and structural network topology in TLE.

METHODS

We studied 34 drug-resistant adult TLE patients and 24 age- and sex-matched healthy controls. Participants underwent a comprehensive neurocognitive battery and multimodal MRI, allowing for large-scale connectomics, and morphological evaluation of subcortical and neocortical regions. Using canonical correlation analysis, we identified a multivariate mode that links cognitive performance to a brain structural network. Our approach was complemented by bootstrap-based hierarchical clustering to derive cognitive subtypes and associated patterns of macroscale connectome anomalies.

RESULTS

Both methodologies provided converging evidence for a close coupling between cognitive impairments across multiple domains and large-scale structural network compromise. Cognitive classes presented with an increasing gradient of abnormalities (increasing cortical and subcortical atrophy and less efficient white matter connectome organization in patients with increasing degrees of cognitive impairments). Notably, network topology characterized cognitive performance better than morphometric measures did.

CONCLUSIONS

Our multivariate approach emphasized a close coupling of cognitive dysfunction and large-scale network anomalies in TLE. Our findings contribute to understand the complexity of structural connectivity regulating the heterogeneous cognitive deficits found in epilepsy.

Dynamic functional connectivity and graph theory metrics in a rat model of temporal lobe epilepsy reveal a preference for brain states with a lower functional connectivity, segregation and integration

Epilepsy is a neurological disorder characterized by recurrent epileptic seizures. The involvement of abnormal functional brain networks in the development of epilepsy and its comorbidities has been demonstrated by electrophysiological and neuroimaging studies in patients with epilepsy. This longitudinal study investigated changes in dynamic functional connectivity (dFC) and network topology during the development of epilepsy using the intraperitoneal kainic acid (IPKA) rat model of temporal lobe epilepsy (TLE). Resting state functional magnetic resonance images (rsfMRI) of 20 IPKA animals and 7 healthy control animals were acquired before and 1, 3, 6, 10 and 16 weeks after status epilepticus (SE) under medetomidine anaesthesia using a 7 T MRI system. Starting from 17 weeks post-SE, hippocampal EEG was recorded to determine the mean daily seizure frequency of each animal. Dynamic FC was assessed by calculating the correlation matrices between fMRI time series of predefined regions of interest within a sliding window of 50 s using a step length of 2 s. The matrices were classified into 6 FC states, each characterized by a correlation matrix, using k-means clustering. In addition, several time-variable graph theoretical network metrics were calculated from the time-varying correlation matrices and classified into 6 states of functional network topology, each characterized by a combination of network metrics. Our results showed that FC states with a lower mean functional connectivity, lower segregation and integration occurred more often in IPKA animals compared to control animals. Functional connectivity also became less variable during epileptogenesis. In addition, average daily seizure frequency was positively correlated with percentage dwell time (i.e. how often a state occurs) in states with high mean functional connectivity, high segregation and integration, and with the number of transitions between states, while negatively correlated with percentage dwell time in states with a low mean functional connectivity, low segregation and low integration. This indicates that animals that dwell in states of higher functional connectivity, higher segregation and higher integration, and that switch more often between states, have more seizures.

A connectome-based mechanistic model of focal cortical dysplasia

Neuroimaging studies have consistently shown distributed brain anomalies in epilepsy syndromes associated with a focal structural lesion, particularly mesiotemporal sclerosis. Conversely, a system-level approach to focal cortical dysplasia has been rarely considered, likely due to methodological difficulties in addressing variable location and topography. Given the known heterogeneity in focal cortical dysplasia histopathology, we hypothesized that lesional connectivity consists of subtypes with distinct structural signatures. Furthermore, in light of mounting evidence for focal anomalies impacting whole-brain systems, we postulated that patterns of focal cortical dysplasia connectivity may exert differential downstream effects on global network topology. We studied a cohort of patients with histologically verified focal cortical dysplasia type II (n = 27), and age- and sex-matched healthy controls (n = 34). We subdivided each lesion into similarly sized parcels and computed their connectivity to large-scale canonical functional networks (or communities). We then dichotomized connectivity profiles of lesional parcels into those belonging to the same functional community as the focal cortical dysplasia (intra-community) and those adhering to other communities (inter-community). Applying hierarchical clustering to community-reconfigured connectome profiles identified three lesional classes with distinct patterns of functional connectivity: decreased intra- and inter-community connectivity, a selective decrease in intra-community connectivity, and increased intra- as well as inter-community connectivity. Hypo-connectivity classes were mainly composed of focal cortical dysplasia type IIB, while the hyperconnected lesions were type IIA. With respect to whole-brain networks, patients with hypoconnected focal cortical dysplasia and marked structural damage showed only mild imbalances, while those with hyperconnected subtle lesions had more pronounced topological alterations. Correcting for interictal epileptic discharges did not impact connectivity patterns. Multivariate structural equation analysis provided a mechanistic model of such complex, diverging interactions, whereby the focal cortical dysplasia structural makeup shapes its functional connectivity, which in turn modulates whole-brain network topology.

Alterations in the functional brain network in a rat model of epileptogenesis: A longitudinal resting state fMRI study

Epilepsy is a neurological disorder characterized by recurrent epileptic seizures. Electrophysiological and neuroimaging studies in patients with epilepsy suggest that abnormal functional brain networks play a role in the development of epilepsy, i.e. epileptogenesis, resulting in the generation of spontaneous seizures and cognitive impairment. In this longitudinal study, we investigated changes in functional brain networks during epileptogenesis in the intraperitoneal kainic acid (IPKA) rat model of temporal lobe epilepsy (TLE) using resting state functional magnetic resonance imaging (rsfMRI) and graph theory. Additionally, we investigated whether these changes are related to the frequency of occurrence of spontaneous epileptic seizures in the chronic phase of epilepsy. Using a 7T MRI system, rsfMRI images were acquired under medetomidine anaesthesia before and 1, 3, 6, 10 and 16 weeks after status epilepticus (SE) induction in 20 IPKA animals and 7 healthy control animals. To obtain a functional network, correlation between fMRI time series of 38 regions of interest (ROIs) was calculated. Then, several graph theoretical network measures were calculated to describe and quantify the network changes. At least 17 weeks post-SE, IPKA animals were implanted with electrodes in the left and right dorsal hippocampus, EEG was measured for 7 consecutive days and spontaneous seizures were counted. Our results show that correlation coefficients of fMRI time series shift to lower values during epileptogenesis, indicating weaker whole brain network connections. Segregation and integration in the functional brain network also decrease, indicating a lower local interconnectivity and a lower overall communication efficiency. Secondly, this study demonstrates that the largest decrease in functional connectivity is observed for the retrosplenial cortex. Finally, post-SE changes in functional connectivity, segregation and integration are correlated with seizure frequency in the IPKA rat model.

White matter lesion loads associated with dynamic functional connectivity within attention network in patients with relapsing-remitting multiple sclerosis

Using time-variant of blood oxygenation level dependent (BOLD) signal to investigate the temporal changes in functional connectivity (FC) between key nodes may shed light on the dynamic characteristics of network. Twenty-two relapsing-remitting multiple sclerosis (RRMS) and 22 well-matched healthy control subjects (HCs) participated in this study. Previously validated key nodes of attention network seeds were defined as spherical regions of interests (ROIs); then, we captured the pattern of dFC using sliding window correlation of ROIs in the RRMS and HCs during rest. Furthermore, correlation analysis between altered dFC of paired-ROIs with clinical measures in RRMS were performed. Compared with the HCs, the RRMS showed: a certain specificity transient pattern of FC of attention network at time window levels, including decreased dFC within dorsal attention network [connections of left intraparietal sulcus (LIPS)-right intraparietal sulcus (RIPS), LIPS-right frontal eye field (RFEF) and left frontal eye field (LFEF)-RIPS] and ventral attention network [connection of right ventral frontal cortex (RVFC)-right temporal parietal junction (RTPJ)], increased dFC between dorsal and ventral attention network (connections of LIPS-RTPJ and LIPS-RVFC). Secondary analysis indicated that the dFC coefficients of the connections of LIPS-RIPS (r = -0.467, P = 0.023) and RVFC-RTPJ (r = -0.452, P = 0.043) were significant negative correlated with the total white matter lesion load. In conclusion, we found that the instantaneous configuration pattern of FC in attention network of RRMS are relate to lesions loads.

Using Low-Frequency Oscillations to Detect Temporal Lobe Epilepsy with Machine Learning

The National Institutes of Health-sponsored Epilepsy Connectome Project aims to characterize connectivity changes in temporal lobe epilepsy (TLE) patients. The magnetic resonance imaging protocol follows that used in the Human Connectome Project, and includes 20 min of resting-state functional magnetic resonance imaging acquired at 3T using 8-band multiband imaging. Glasser parcellation atlas was combined with the FreeSurfer subcortical regions to generate resting-state functional connectivity (RSFC), amplitude of low-frequency fluctuations (ALFFs), and fractional ALFF measures. Seven different frequency ranges such as Slow-5 (0.01-0.027 Hz) and Slow-4 (0.027-0.073 Hz) were selected to compute these measures. The goal was to train machine learning classification models to discriminate TLE patients from healthy controls, and to determine which combination of the resting state measure and frequency range produced the best classification model. The samples included age- and gender-matched groups of 60 TLE patients and 59 healthy controls. Three traditional machine learning models were trained: support vector machine, linear discriminant analysis, and naive Bayes classifier. The highest classification accuracy was obtained using RSFC measures in the Slow-4 + 5 band (0.01-0.073 Hz) as features. Leave-one-out cross-validation accuracies were ∼83%, with receiver operating characteristic area-under-the-curve reaching close to 90%. Increased connectivity from right area posterior 9-46v in TLE patients contributed to the high accuracies. With increased sample sizes in the near future, better machine learning models will be trained not only to aid the diagnosis of TLE, but also as a tool to understand this brain disorder.

Identification of epileptic brain states by dynamic functional connectivity analysis of simultaneous EEG-fMRI: a dictionary learning approach

Most fMRI studies of the brain's intrinsic functional connectivity (FC) have assumed that this is static; however, it is now clear that it changes over time. This is particularly relevant in epilepsy, which is characterized by a continuous interchange between epileptic and normal brain states associated with the occurrence of epileptic activity. Interestingly, recurrent states of dynamic FC (dFC) have been found in fMRI data using unsupervised learning techniques, assuming either their sparse or non-sparse combination. Here, we propose an l₁-norm regularized dictionary learning (l₁-DL) approach for dFC state estimation, which allows an intermediate and flexible degree of sparsity in time, and demonstrate its application in the identification of epilepsy-related dFC states using simultaneous EEG-fMRI data. With this l₁-DL approach, we aim to accommodate a potentially varying degree of sparsity upon the interchange between epileptic and non-epileptic dFC states. The simultaneous recording of the EEG is used to extract time courses representative of epileptic activity, which are incorporated into the fMRI dFC state analysis to inform the selection of epilepsy-related dFC states. We found that the proposed l₁-DL method performed best at identifying epilepsy-related dFC states, when compared with two alternative methods of extreme sparsity (k-means clustering, maximum; and principal component analysis, minimum), as well as an l₀-norm regularization framework (l₀-DL), with a fixed amount of temporal sparsity. We further showed that epilepsy-related dFC states provide novel insights into the dynamics of epileptic networks, which go beyond the information provided by more conventional EEG-correlated fMRI analysis, and which were concordant with the clinical profile of each patient. In addition to its application in epilepsy, our study provides a new dFC state identification method of potential relevance for studying brain functional connectivity dynamics in general.

Prognostic factors determining poor postsurgical outcomes of mesial temporal lobe epilepsy

Objectives

To investigate the long-term postoperative outcomes and predictive factors associated with poor surgical outcomes in mesial temporal lobe epilepsy (MTLE).

Materials and methods

We enrolled patients with MTLE who underwent resective surgery at single university-affiliated hospital. Surgical outcomes were determined using a modified Engel classification at the 2^nd and 5^th years after surgery and the last time of follow-up.

Results

The mean duration of follow-up after surgery was 7.6 ± 3.7 years (range, 5.0–21.0 years). 334 of 400 patients (83.5%) were seizure-free at the 5^th postoperative year. Significant predictive factors of a poor outcome at the 5^th year were a history of generalized tonic clonic (GTC) seizures (odds ratio, OR; 2.318), bi-temporal interictal epileptiform discharge (IED) (OR; 3.107), bilateral hippocampal sclerosis (HS) (OR; 5.471), unilateral HS and combined extra-hippocampal lesion (OR; 5.029), and bi-temporal hypometabolism (BTH) (OR; 4.438). Bi-temporal IED (hazard ratio, HR; 2.186), BTH (HR; 2.043), bilateral HS (HR; 2.541) and unilateral HS and combined extra-hippocampal lesion (HR; 2.75) were independently associated with seizure recurrence. We performed a subgroup analysis of 208 patients with unilateral HS, and their independent predictors of a poor outcome at the 5^th year were BTH (OR; 5.838) and tailored hippocampal resection (OR; 11.053).

Conclusion

This study demonstrates that 16.5% of MTLE patients had poor long-term outcomes after surgery. Bilateral involvement in electrophysiological and imaging studies predicts poor surgical outcomes in MTLE patients.

Spatio-temporal dynamics of resting-state brain networks improve single-subject prediction of schizophrenia diagnosis

Correlation in functional MRI activity between spatially separated brain regions can fluctuate dynamically when an individual is at rest. These dynamics are typically characterized temporally by measuring fluctuations in functional connectivity between brain regions that remain fixed in space over time. Here, dynamics in functional connectivity were characterized in both time and space. Temporal dynamics were mapped with sliding-window correlation, while spatial dynamics were characterized by enabling network regions to vary in size (shrink/grow) over time according to the functional connectivity profile of their constituent voxels. These temporal and spatial dynamics were evaluated as biomarkers to distinguish schizophrenia patients from controls, and compared to current biomarkers based on static measures of resting-state functional connectivity. Support vector machine classifiers were trained using: (a) static, (b) dynamic in time, (c) dynamic in space, and (d) dynamic in time and space characterizations of functional connectivity within canonical resting-state brain networks. Classifiers trained on functional connectivity dynamics mapped over both space and time predicted diagnostic status with accuracy exceeding 91%, whereas utilizing only spatial or temporal dynamics alone yielded lower classification accuracies. Static measures of functional connectivity yielded the lowest accuracy (79.5%). Compared to healthy comparison individuals, schizophrenia patients generally exhibited functional connectivity that was reduced in strength and more variable. Robustness was established with replication in an independent dataset. The utility of biomarkers based on temporal and spatial functional connectivity dynamics suggests that resting-state dynamics are not trivially attributable to sampling variability and head motion.

Degenerate time-dependent network dynamics anticipate seizures in human epileptic brain

Epileptic seizures are known to follow specific changes in brain dynamics. While some algorithms can nowadays robustly detect these changes, a clear understanding of the mechanism by which these alterations occur and generate seizures is still lacking. Here, we provide crossvalidated evidence that such changes are initiated by an alteration of physiological network state dynamics. Specifically, our analysis of long intracranial electroencephalography (iEEG) recordings from a group of 10 patients identifies a critical phase of a few hours in which time-dependent network states become less variable ("degenerate"), and this phase is followed by a global functional connectivity reduction before seizure onset. This critical phase is characterized by an abnormal occurrence of highly correlated network instances and is shown to be particularly associated with the activity of the resected regions in patients with validated postsurgical outcome. Our approach characterizes preseizure network dynamics as a cascade of 2 sequential events providing new insights into seizure prediction and control.

Understanding and predicting the generation of seizures in epileptic patients is fundamental to improving the quality of life of the more than 1% of the world population who suffer from this illness. Although seizure prediction has made important advances over the last decade, there is a lack of understanding of the common principles explaining the transitions that brain activity undergoes before a seizure. In this study, we characterized this transition from a novel perspective grounded on the mathematical analysis of continuous recordings inside the brains of epileptic patients over several days using depth electrodes. We show that the critical period preceding a seizure unfolds in a two-stage process. It begins with a phase of several hours when the highly correlated activity in the preceding days is altered, and it proceeds with a second, shorter phase of decrease in global connectivity before the seizure onset. Furthermore, our analysis reveals that these global alterations are more locally manifested in areas that are selected for surgical treatment. Our study suggests that preseizure activity might follow global stereotyped dynamics that could be targeted and modulated to prevent epileptic seizures.

Task activation and functional connectivity show concordant memory laterality in temporal lobe epilepsy

OBJECTIVE

In epilepsy, asymmetries in the organization of mesial temporal lobe (MTL) functions help determine the cognitive risk associated with procedures such as anterior temporal lobectomy. Past studies have investigated the change/shift in a visual episodic memory laterality index (LI) in mesial temporal lobe structures through functional magnetic resonance imaging (fMRI) task activations. Here, we examine whether underlying task-related functional connectivity (FC) is concordant with such standard fMRI laterality measures.

METHODS

A total of 56 patients with temporal lobe epilepsy (TLE) (Left TLE [LTLE]: 31; Right TLE [RTLE]: 25) and 34 matched healthy controls (HC) underwent fMRI scanning during performance of a scene encoding task (SET). We assessed an activation-based LI of the hippocampal gyrus (HG) and parahippocampal gyrus (PHG) during the SET and its correspondence with task-related FC measures.

RESULTS

Analyses involving the HG and PHG showed that the patients with LTLE had a consistently higher LI (right-lateralized) than that of the HC and group with RTLE, indicating functional reorganization. The patients with RTLE did not display a reliable contralateral shift away from the pathology, with the mesial structures showing quite distinct laterality patterns (HG, no laterality bias; PHG, no evidence of LI shift). The FC data for the group with LTLE provided confirmation of reorganization effects, revealing that a rightward task LI may be based on underlying connections between several left-sided regions (middle/superior occipital and left medial frontal gyri) and the right PHG. The FCs between the right HG and left anterior cingulate/medial frontal gyri were also observed in LTLE. Importantly, the data demonstrate that the areas involved in the LTLE task activation shift to the right hemisphere showed a corresponding increase in task-related FCs between the hemispheres.

SIGNIFICANCE

Altered laterality patterns based on mesial temporal lobe epilepsy (MTLE) pathology manifest as several different phenotypes, varying according to side of seizure onset and the specific mesial structures involved. There is good correspondence between task LI activation and FC patterns in the setting of LTLE, suggesting that reliable visual episodic memory reorganization may require both a shift in nodal activation and a change in nodal connectivity with mesial temporal structures involved in memory.

Dynamic network dysfunction in cocaine dependence: Graph theoretical metrics and stop signal reaction time

Graphic theoretical metrics have become increasingly popular in characterizing functional connectivity of neural networks and how network connectivity is compromised in neuropsychiatric illnesses. Here, we add to this literature by describing dynamic network connectivities of 78 cocaine dependent (CD) and 85 non-drug using healthy control (HC) participants who underwent fMRI during performance of a stop signal task (SST). Compared to HC, CD showed prolonged stop signal reaction time (SSRT), consistent with deficits in response inhibition. In graph theoretical analysis of dynamic functional connectivity, we examined temporal flexibility and spatiotemporal diversity of 14 networks covering the whole brain. Temporal flexibility quantifies how frequently a brain region interacts with regions of other communities across time, with high temporal flexibility indicating that a region interacts predominantly with regions outside its own community. Spatiotemporal diversity quantifies how uniformly a brain region interacts with regions in other communities over time, with high spatiotemporal diversity indicating that the interactions are more evenly distributed across communities. Compared to HC, CD exhibited decreased temporal flexibility and increased spatiotemporal diversity in the great majority of neural networks. The graph metric measures of the default mode network negatively correlated with SSRT in CD but not HC. The findings are consistent with diminished temporal flexibility and a compensatory increase in spatiotemporal diversity, in association with impairment of a critical executive function, in cocaine addiction. More broadly, the findings suggest that graph theoretical metrics provide new insights for connectivity analyses to elucidate network dysfunction that may elude conventional measures.

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Cocaine addiction (CA) is associated with prolonged stop signal reaction time (SSRT).

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CA is associated with decreased temporal flexibility (TF) of neural networks.

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CA is associated with increased spatial temporal diversity (STD) of neural networks.

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The TF and STD of default mode network correlated negatively with SSRT in CA.

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Dynamic connectivity captures network dysfunction in link with inhibition deficits in CA.

Methods and Considerations for Dynamic Analysis of Functional MR Imaging Data

Functional MR imaging (fMR imaging) studies have recently begun to examine spontaneous changes in interregional interactions (functional connectivity) over seconds to minutes, and their relation to natural shifts in cognitive and physiologic states. This practice opens the potential for uncovering structured, transient configurations of coordinated brain activity whose features may provide novel cognitive and clinical biomarkers. However, analysis of these time-varying phenomena requires careful differentiation between neural and nonneural contributions to the fMR imaging signal and thorough validation and statistical testing. In this article, the authors present an overview of methodological and interpretational considerations in this emerging field.

Neural and metabolic basis of dynamic resting state fMRI

Resting state fMRI (rsfMRI) as a technique showed much initial promise for use in psychiatric and neurological diseases where diagnosis and treatment were difficult. To realize this promise, many groups have moved towards examining "dynamic rsfMRI," which relies on the assumption that rsfMRI measurements on short time scales remain relevant to the underlying neural and metabolic activity. Many dynamic rsfMRI studies have demonstrated differences between clinical or behavioral groups beyond what static rsfMRI measured, suggesting a neurometabolic basis. Correlative studies combining dynamic rsfMRI and other physiological measurements have supported this. However, they also indicate multiple mechanisms and, if using correlation alone, it is difficult to separate cause and effect. Hypothesis-driven studies are needed, a few of which have begun to illuminate the underlying neurometabolic mechanisms that shape observed differences in dynamic rsfMRI. While the number of potential noise sources, potential actual neurometabolic sources, and methodological considerations can seem overwhelming, dynamic rsfMRI provides a rich opportunity in systems neuroscience. Even an incrementally better understanding of the neurometabolic basis of dynamic rsfMRI would expand rsfMRI's research and clinical utility, and the studies described herein take the first steps on that path forward.

Magnetic resonance imaging connectivity for the prediction of seizure outcome in temporal lobe epilepsy

OBJECTIVE

Currently, approximately 60-70% of patients with unilateral temporal lobe epilepsy (TLE) remain seizure-free 3 years after surgery. The goal of this work was to develop a presurgical connectivity-based biomarker to identify those patients who will have an unfavorable seizure outcome 1-year postsurgery.

METHODS

Resting-state functional and diffusion-weighted 3T magnetic resonance imaging (MRI) was acquired from 22 unilateral (15 right, 7 left) patients with TLE and 35 healthy controls. A seizure propagation network was identified including ipsilateral (to seizure focus) and contralateral hippocampus, thalamus, and insula, with bilateral midcingulate and precuneus. Between each pair of regions, functional connectivity based on correlations of low frequency functional MRI signals, and structural connectivity based on streamline density of diffusion MRI data were computed and transformed to metrics related to healthy controls of the same age.

RESULTS

A consistent connectivity pattern representing the network expected in patients with seizure-free outcome was identified using eight patients who were seizure-free at 1-year postsurgery. The hypothesis that increased similarity to the model would be associated with better seizure outcome was tested in 14 other patients (Engel class IA, seizure-free: n = 5; Engel class IB-II, favorable: n = 4; Engel class III-IV, unfavorable: n = 5) using two similarity metrics: Pearson correlation and Euclidean distance. The seizure-free connectivity model successfully separated all the patients with unfavorable outcome from the seizure-free and favorable outcome patients (p = 0.0005, two-tailed Fisher's exact test) through the combination of the two similarity metrics with 100% accuracy. No other clinical and demographic predictors were successful in this regard.

SIGNIFICANCE

This work introduces a methodologic framework to assess individual patients, and demonstrates the ability to use network connectivity as a potential clinical tool for epilepsy surgery outcome prediction after more comprehensive validation.

The dynamics of functional connectivity in neocortical focal epilepsy

Focal epilepsy is characterised by paroxysmal events, reflecting changes in underlying local brain networks. To capture brain network activity at the maximal temporal resolution of the acquired functional magnetic resonance imaging (fMRI) data, we have previously developed a novel analysis framework called Dynamic Regional Phase Synchrony (DRePS). DRePS measures instantaneous mean phase coherence within neighbourhoods of brain voxels. We use it here to examine how the dynamics of the functional connections of regional brain networks are altered in neocortical focal epilepsy. Using task-free fMRI data from 21 subjects with focal epilepsy and 21 healthy controls, we calculated the power spectral density of DRePS, which is a measure of signal variability in local connectivity estimates. Whole-brain averaged power spectral density of DRePS, or signal variability of local connectivity, was significantly higher in epilepsy subjects compared to healthy controls. Maximal increase in DRePS spectral power was seen in bilateral inferior frontal cortices, ipsilateral mid-cingulate gyrus, superior temporal gyrus, caudate head, and contralateral cerebellum. Our results provide further evidence of common brain abnormalities across people with focal epilepsy. We postulate that dynamic changes in specific cortical brain areas may help maintain brain function in the presence of pathological epileptiform network activity in neocortical focal epilepsy.

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Focal epilepsy is a disease defined by its inherent paroxysmal brain changes.

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However, most fMRI connectivity methods do not capture dynamic brain network activity.

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We use our recently developed method, DRePS, to identify abnormal dynamic brain networks in focal epilepsy.

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Fluctuations of dynamic brain network activity were increased in the frontal-temporal cortex, cingulum and cerebellum, in focal epilepsy.

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These regions may be involved in (re-)gaining cortical homeostasis in an otherwise abnormal brain.

The dynamic functional connectome: State-of-the-art and perspectives

Resting-state functional magnetic resonance imaging (fMRI) has highlighted the rich structure of brain activity in absence of a task or stimulus. A great effort has been dedicated in the last two decades to investigate functional connectivity (FC), i.e. the functional interplay between different regions of the brain, which was for a long time assumed to have stationary nature. Only recently was the dynamic behaviour of FC revealed, showing that on top of correlational patterns of spontaneous fMRI signal fluctuations, connectivity between different brain regions exhibits meaningful variations within a typical resting-state fMRI experiment. As a consequence, a considerable amount of work has been directed to assessing and characterising dynamic FC (dFC), and several different approaches were explored to identify relevant FC fluctuations. At the same time, several questions were raised about the nature of dFC, which would be of interest only if brought back to a neural origin. In support of this, correlations with electroencephalography (EEG) recordings, demographic and behavioural data were established, and various clinical applications were explored, where the potential of dFC could be preliminarily demonstrated. In this review, we aim to provide a comprehensive description of the dFC approaches proposed so far, and point at the directions that we see as most promising for the future developments of the field. Advantages and pitfalls of dFC analyses are addressed, helping the readers to orient themselves through the complex web of available methodologies and tools.

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

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

Resting-state functional connectivity by independent component analysis-based markers corresponds to areas of initial seizure propagation established by prior modalities from the hypothalamus

OBJECTIVE

The aims of this study were to evaluate a clinically practical functional connectivity protocol designed to blindly identify the corresponding areas of initial seizure propagation and also to differentiate these areas from remote secondary areas affected by seizure. The patients in this cohort had intractable epilepsy caused by intrahypothalamic hamartoma, which is the location of the ictal focus. The ictal propagation pathway is homogeneous and established, thus creating the optimum situation for the proposed method validation study.

METHODS

Twelve patients with seizures from hypothalamic hamartoma and 6 normal control patients underwent resting state functional MRI, using independent component analysis to identify network differences in patients. This was followed by seed-based connectivity measures to determine the extent of functional connectivity derangement between hypothalamus and these areas. The areas with significant change in connectivity were compared with the results of prior studies' modalities used to evaluate seizure propagation.

RESULTS

The left amygdala-parahippocampal gyrus area, cingulate gyrus, and occipito-temporal gyrus demonstrated the highest derangement in connectivity with the hypothalamus, p < 0.01, corresponding to the initial seizure propagation areas established by prior modalities. Areas of secondary ictal propagation were differentiated from these initial locations by first being identified as an abnormal neuronal signal source via independent component analysis, but did not show significant connectivity directly with the known ictal focus.

CONCLUSION

Non-invasive connectivity measures correspond to areas of initial ictal propagation and differentiate such areas from secondary ictal propagation, which may aid in ictal focus surgical disconnection planning and support the use of this newer modality for adjunctive information in epilepsy surgery evaluation.

A Unified Estimation Framework for State-Related Changes in Effective Brain Connectivity

OBJECTIVE

This paper addresses the critical problem of estimating time-evolving effective brain connectivity. Current approaches based on sliding window analysis or time-varying coefficient models do not simultaneously capture both slow and abrupt changes in causal interactions between different brain regions.

METHODS

To overcome these limitations, we develop a unified framework based on a switching vector autoregressive (SVAR) model. Here, the dynamic connectivity regimes are uniquely characterized by distinct vector autoregressive (VAR) processes and allowed to switch between quasi-stationary brain states. The state evolution and the associated directed dependencies are defined by a Markov process and the SVAR parameters. We develop a three-stage estimation algorithm for the SVAR model: 1) feature extraction using time-varying VAR (TV-VAR) coefficients, 2) preliminary regime identification via clustering of the TV-VAR coefficients, 3) refined regime segmentation by Kalman smoothing and parameter estimation via expectation-maximization algorithm under a state-space formulation, using initial estimates from the previous two stages.

RESULTS

The proposed framework is adaptive to state-related changes and gives reliable estimates of effective connectivity. Simulation results show that our method provides accurate regime change-point detection and connectivity estimates. In real applications to brain signals, the approach was able to capture directed connectivity state changes in functional magnetic resonance imaging data linked with changes in stimulus conditions, and in epileptic electroencephalograms, differentiating ictal from nonictal periods.

CONCLUSION

The proposed framework accurately identifies state-dependent changes in brain network and provides estimates of connectivity strength and directionality.

SIGNIFICANCE

The proposed approach is useful in neuroscience studies that investigate the dynamics of underlying brain states.