Visualization of the Spatiotemporal Propagation of Interictal Spikes in Temporal Lobe Epilepsy: A MEG Pilot Study.

Magnetoencephalography (MEG) is clinically used to localize interictal spikes in discrete brain areas of epilepsy patients through the equivalent current dipole (ECD) method, but does not account for the temporal dynamics of spike activity. Recent studies found that interictal spike propagation beyond the temporal lobe may be associated with worse postsurgical outcomes, but studies using whole-brain data such as in MEG remain limited. In this pilot study, we developed a tool that visualizes the spatiotemporal dynamics of interictal MEG spikes normalized to spike-free sleep activity to assess their onset and propagation patterns in patients with temporal lobe epilepsy (TLE). We extracted interictal source data containing focal epileptiform activity in awake and asleep states from seven patients whose MEG ECD clusters localized to the temporal lobe and normalized the data against spike-free sleep recordings. We calculated the normalized activity over time per cortical label, confirmed maximal activity at onset, and mapped the activity over a 10 ms interval onto each patient's brain using a custom-built Multi-Modal Visualization Tool. The onset of activity in all patients appeared near the clinically determined epileptogenic zone. By 10 ms, four of the patients had propagated source activity restricted to within the temporal lobe, and three had propagated source activity spread to extratemporal regions. Using this tool, we show that noninvasively identifying the onset and propagation of interictal spike activity in MEG can be achieved, which may help provide further insight into epileptic networks and guide surgical planning and interventions in patients with TLE.

Regional healthy brain activity, glioma occurrence and symptomatology.

It is unclear why exactly gliomas show preferential occurrence in certain brain areas. Increased spiking activity around gliomas leads to faster tumour growth in animal models, while higher non-invasively measured brain activity is related to shorter survival in patients. However, it is unknown how regional intrinsic brain activity, as measured in healthy controls, relates to glioma occurrence. We first investigated whether gliomas occur more frequently in regions with intrinsically higher brain activity. Second, we explored whether intrinsic cortical activity at individual patients' tumour locations relates to tumour and patient characteristics. Across three cross-sectional cohorts, 413 patients were included. Individual tumour masks were created. Intrinsic regional brain activity was assessed through resting-state magnetoencephalography acquired in healthy controls and source-localized to 210 cortical brain regions. Brain activity was operationalized as: (i) broadband power; and (ii) offset of the aperiodic component of the power spectrum, which both reflect neuronal spiking of the underlying neuronal population. We additionally assessed (iii) the slope of the aperiodic component of the power spectrum, which is thought to reflect the neuronal excitation/inhibition ratio. First, correlation coefficients were calculated between group-level regional glioma occurrence, as obtained by concatenating tumour masks across patients, and group-averaged regional intrinsic brain activity. Second, intrinsic brain activity at specific tumour locations was calculated by overlaying patients' individual tumour masks with regional intrinsic brain activity of the controls and was associated with tumour and patient characteristics. As proposed, glioma preferentially occurred in brain regions characterized by higher intrinsic brain activity in controls as reflected by higher offset. Second, intrinsic brain activity at patients' individual tumour locations differed according to glioma subtype and performance status: the most malignant isocitrate dehydrogenase-wild-type glioblastoma patients had the lowest excitation/inhibition ratio at their individual tumour locations as compared to isocitrate dehydrogenase-mutant, 1p/19q-codeleted glioma patients, while a lower excitation/inhibition ratio related to poorer Karnofsky Performance Status, particularly in codeleted glioma patients. In conclusion, gliomas more frequently occur in cortical brain regions with intrinsically higher activity levels, suggesting that more active regions are more vulnerable to glioma development. Moreover, indices of healthy, intrinsic excitation/inhibition ratio at patients' individual tumour locations may capture both tumour biology and patients' performance status. These findings contribute to our understanding of the complex and bidirectional relationship between normal brain functioning and glioma growth, which is at the core of the relatively new field of 'cancer neuroscience'.

Diet modulates brain network stability, a biomarker for brain aging, in young adults.

Epidemiological studies suggest that insulin resistance accelerates progression of age-based cognitive impairment, which neuroimaging has linked to brain glucose hypometabolism. As cellular inputs, ketones increase Gibbs free energy change for ATP by 27% compared to glucose. Here we test whether dietary changes are capable of modulating sustained functional communication between brain regions (network stability) by changing their predominant dietary fuel from glucose to ketones. We first established network stability as a biomarker for brain aging using two large-scale (n = 292, ages 20 to 85 y; n = 636, ages 18 to 88 y) 3 T functional MRI (fMRI) datasets. To determine whether diet can influence brain network stability, we additionally scanned 42 adults, age < 50 y, using ultrahigh-field (7 T) ultrafast (802 ms) fMRI optimized for single-participant-level detection sensitivity. One cohort was scanned under standard diet, overnight fasting, and ketogenic diet conditions. To isolate the impact of fuel type, an independent overnight fasted cohort was scanned before and after administration of a calorie-matched glucose and exogenous ketone ester (d-ß-hydroxybutyrate) bolus. Across the life span, brain network destabilization correlated with decreased brain activity and cognitive acuity. Effects emerged at 47 y, with the most rapid degeneration occurring at 60 y. Networks were destabilized by glucose and stabilized by ketones, irrespective of whether ketosis was achieved with a ketogenic diet or exogenous ketone ester. Together, our results suggest that brain network destabilization may reflect early signs of hypometabolism, associated with dementia. Dietary interventions resulting in ketone utilization increase available energy and thus may show potential in protecting the aging brain.

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.

Cortico-striato-thalamo-cerebellar networks of structural covariance underlying different epilepsy syndromes associated with generalized tonic-clonic seizures.

Generalized tonic-clonic seizures (GTCS) are the severest and most remarkable clinical expressions of human epilepsy. Cortical, subcortical, and cerebellar structures, organized with different network patterns, underlying the pathophysiological substrates of genetic associated epilepsy with GTCS (GE-GTCS) and focal epilepsy associated with focal to bilateral tonic-clonic seizure (FE-FBTS). Structural covariance analysis can delineate the features of epilepsy network related with long-term effects from seizure. Morphometric MRI data of 111 patients with GE-GTCS, 111 patients with FE-FBTS and 111 healthy controls were studied. Cortico-striato-thalao-cerebellar networks of structural covariance within the gray matter were constructed using a Winner-take-all strategy with five cortical parcellations. Comparisons of structural covariance networks were conducted using permutation tests, and module effects of disease duration on networks were conducted using GLM model. Both patient groups showed increased connectivity of structural covariance relative to controls, mainly within the striatum and thalamus, and mostly correlated with the frontal, motor, and somatosensory cortices. Connectivity changes increased as a function of epilepsy durations. FE-FBTS showed more intensive and extensive gray matter changes with volumetric loss and connectivity increment than GE-GTCS. Our findings implicated cortico-striato-thalamo-cerebellar network changes at a large temporal scale in GTCS, with FE-FBTS showing more severe network disruption. The study contributed novel imaging evidence for understanding the different epilepsy syndromes associated with generalized seizures.

Delayed brain development of Rolandic epilepsy profiled by deep learning-based neuroanatomic imaging.

OBJECTIVES: Although Rolandic epilepsy (RE) has been regarded as a brain developmental disorder, neuroimaging studies have not yet ascertained whether RE has brain developmental delay. This study employed deep learning-based neuroanatomic biomarker to measure the changed feature of "brain age" in RE. METHODS: The study constructed a 3D-CNN brain age prediction model through 1155 cases of typically developing children's morphometric brain MRI from open-source datasets and further applied to a local dataset of 167 RE patients and 107 typically developing children. The brain-predicted age difference was measured to quantitatively estimate brain age changes in RE and further investigated the relevancies with cognitive and clinical variables. RESULTS: The brain age estimation network model presented a good performance for brain age prediction in typically developing children. The children with RE showed a 0.45-year delay of brain age by contrast with typically developing children. Delayed brain age was associated with neuroanatomic changes in the Rolandic regions and also associated with cognitive dysfunction of attention. CONCLUSION: This study provided neuroimaging evidence to support the notion that RE has delayed brain development. KEY POINTS: • The children with Rolandic epilepsy showed imaging phenotypes of delayed brain development with increased GM volume and decreased WM volume in the Rolandic regions. • The children with Rolandic epilepsy had a 0.45-year delay of brain-predicted age by comparing with typically developing children, using 3D-CNN-based brain age prediction model. • The delayed brain age was associated with morphometric changes in the Rolandic regions and attentional deficit in Rolandic epilepsy.

Contralateral Preoperative Resting-State Functional MRI Network Integration Is Associated with Surgical Outcome in Temporal Lobe Epilepsy.

Background Although most patients with medically refractory temporal lobe epilepsy (TLE) experience seizure freedom after anterior temporal lobectomy, approximately 40% may continue to have seizures. Functional network integration, as measured with preoperative resting-state functional MRI, may help stratify patients who are more likely to experience postoperative seizure freedom. Purpose To relate preoperative resting-state functional MRI and surgical outcome in patients with medically refractory TLE. Materials and Methods Data from patients with medically intractable TLE were retrospectively analyzed. Patients underwent preoperative resting-state functional MRI between March 2010 and April 2013 and subsequent unilateral anterior temporal lobectomy. Postoperative seizure-free status was categorized using the Engel Epilepsy Surgery Outcome Scale. Global and regional resting-state functional MRI network properties on preoperative functional MRI scans related to integration were calculated and statistically compared between patients who experienced complete postoperative seizure freedom (Engel class IA) and all others (Engel class IB to class IV) using t tests and multiple logistic regression. Results Forty patients (mean age, 34 years ± 15 [standard deviation]; 21 female) were evaluated. Preoperative global network integration was different (P = .01) between patients who experienced seizure freedom after surgery and all other patients, with 9% lower leaf fraction and 10% lower tree hierarchy in patients with ongoing seizures. Preoperative regional network integration in the contralateral temporoinsular region was different (P = .04) between patients in these two groups. Specifically, the group-level leaf proportion was 59% lower in the entorhinal cortex, 73% lower in the inferior temporal gyrus, 43% lower in the temporal pole, and 69% lower in the insula in patients with ongoing seizures after surgery. When using multivariate regression, contralateral temporoinsular leaf proportion (P = .002) and epilepsy duration (P = .04) were predictive of postoperative seizure freedom, while age (P > .70) and age at seizure onset (P > .50) were not. Conclusion Lower network integration globally and involving the contralateral temporoinsular cortex on preoperative resting-state functional MRI scans is associated with ongoing postoperative seizures in patients with temporal lobe epilepsy. © RSNA, 2020.

Persistent abnormalities in Rolandic thalamocortical white matter circuits in childhood epilepsy with centrotemporal spikes.

OBJECTIVE: Childhood epilepsy with centrotemporal spikes (CECTS) is a common, focal, transient, developmental epilepsy syndrome characterized by unilateral or bilateral, independent epileptiform spikes in the Rolandic regions of unknown etiology. Given that CECTS presents during a period of dramatic white matter maturation and thatspikes in CECTS are activated during non-rapid eye movement (REM) sleep, we hypothesized that children with CECTS would have aberrant development of white matter connectivity between the thalamus and the Rolandic cortex. We further tested whether Rolandic thalamocortical structural connectivity correlates with spike rate during non-REM sleep. METHODS: Twenty-three children with CECTS (age = 8-15 years) and 19 controls (age = 7-15 years) underwent 3-T structural and diffusion-weighted magnetic resonance imaging and 72-electrode electroencephalographic recordings. Thalamocortical structural connectivity to Rolandic and non-Rolandic cortices was quantified using probabilistic tractography. Developmental changes in connectivity were compared between groups using bootstrap analyses. Longitudinal analysis was performed in four subjects with 1-year follow-up data. Spike rate was quantified during non-REM sleep using manual and automated techniques and compared to Rolandic connectivity using regression analyses. RESULTS: Children with CECTS had aberrant development of thalamocortical connectivity to the Rolandic cortex compared to controls (P = .01), where the expected increase in connectivity with age was not observed in CECTS. There was no difference in the development of thalamocortical connectivity to non-Rolandic regions between CECTS subjects and controls (P = .19). Subjects with CECTS observed longitudinally had reductions in thalamocortical connectivity to the Rolandic cortex over time. No definite relationship was found between Rolandic connectivity and non-REM spike rate (P > .05). SIGNIFICANCE: These data provide evidence that abnormal maturation of thalamocortical white matter circuits to the Rolandic cortex is a feature of CECTS. Our data further suggest that the abnormalities in these tracts do not recover, but are increasingly dysmature over time, implicating a permanent but potentially compensatory process contributing to disease resolution.

Scalp recorded spike ripples predict seizure risk in childhood epilepsy better than spikes.

In the past decade, brief bursts of fast oscillations in the ripple range have been identified in the scalp EEG as a promising non-invasive biomarker for epilepsy. However, investigation and clinical application of this biomarker have been limited because standard approaches to identify these brief, low amplitude events are difficult, time consuming, and subjective. Recent studies have demonstrated that ripples co-occurring with epileptiform discharges ('spike ripple events') are easier to detect than ripples alone and have greater pathological significance. Here, we used objective techniques to quantify spike ripples and test whether this biomarker predicts seizure risk in childhood epilepsy. We evaluated spike ripples in scalp EEG recordings from a prospective cohort of children with a self-limited epilepsy syndrome, benign epilepsy with centrotemporal spikes, and healthy control children. We compared the rate of spike ripples between children with epilepsy and healthy controls, and between children with epilepsy during periods of active disease (active, within 1 year of seizure) and after a period of sustained seizure-freedom (seizure-free, >1 year without seizure), using semi-automated and automated detection techniques. Spike ripple rate was higher in subjects with active epilepsy compared to healthy controls (P = 0.0018) or subjects with epilepsy who were seizure-free ON or OFF medication (P = 0.0018). Among epilepsy subjects with spike ripples, each month seizure-free decreased the odds of a spike ripple by a factor of 0.66 [95% confidence interval (0.47, 0.91), P = 0.021]. Comparing the diagnostic accuracy of the presence of at least one spike ripple versus a classic spike event to identify group, we found comparable sensitivity and negative predictive value, but greater specificity and positive predictive value of spike ripples compared to spikes (P = 0.016 and P = 0.006, respectively). We found qualitatively consistent results using a fully automated spike ripple detector, including comparison with an automated spike detector. We conclude that scalp spike ripple events identify disease and track with seizure risk in this epilepsy population, using both semi-automated and fully automated detection methods, and that this biomarker outperforms analysis of spikes alone in categorizing seizure risk. These data provide evidence that spike ripples are a specific non-invasive biomarker for seizure risk in benign epilepsy with centrotemporal spikes and support future work to evaluate the utility of this biomarker to guide medication trials and tapers in these children and predict seizure risk in other at-risk populations.

Dynamic connectivity modulates local activity in the core regions of the default-mode network.

Segregation and integration are distinctive features of large-scale brain activity. Although neuroimaging studies have been unraveling their neural correlates, how integration takes place over segregated modules remains elusive. Central to this problem is the mechanism by which a brain region adjusts its activity according to the influence it receives from other regions. In this study, we explore how dynamic connectivity between two regions affects the neural activity within a participating region. Combining functional magnetic resonance imaging (fMRI) and magnetoencephalography (MEG) in the same group of subjects, we analyzed resting-state data from the core of the default-mode network. We observed directed influence from the posterior cingulate cortex (PCC) to the anterior cingulate cortex (ACC) in the 10-Hz range. This time-varying influence was associated with the power alteration in the ACC: strong influence corresponded with a decrease of power around 13-16 Hz and an increase of power in the lower (1-7 Hz) and higher (30-55 Hz) ends of the spectrum. We also found that the amplitude of the 30- to 55-Hz activity was coupled to the phase of the 3- to 4-Hz activity in the ACC. These results characterized the local spectral changes associated with network interactions. The specific spectral information both highlights the functional roles of PCC-ACC connectivity in the resting state and provides insights into the dynamic relationship between local activity and coupling dynamics of a network.

Maturation trajectories of cortical resting-state networks depend on the mediating frequency band.

The functional significance of resting state networks and their abnormal manifestations in psychiatric disorders are firmly established, as is the importance of the cortical rhythms in mediating these networks. Resting state networks are known to undergo substantial reorganization from childhood to adulthood, but whether distinct cortical rhythms, which are generated by separable neural mechanisms and are often manifested abnormally in psychiatric conditions, mediate maturation differentially, remains unknown. Using magnetoencephalography (MEG) to map frequency band specific maturation of resting state networks from age 7 to 29 in 162 participants (31 independent), we found significant changes with age in networks mediated by the beta (13-30 Hz) and gamma (31-80 Hz) bands. More specifically, gamma band mediated networks followed an expected asymptotic trajectory, but beta band mediated networks followed a linear trajectory. Network integration increased with age in gamma band mediated networks, while local segregation increased with age in beta band mediated networks. Spatially, the hubs that changed in importance with age in the beta band mediated networks had relatively little overlap with those that showed the greatest changes in the gamma band mediated networks. These findings are relevant for our understanding of the neural mechanisms of cortical maturation, in both typical and atypical development.

Loss of brain graph network efficiency in alcohol dependence.

Alcohol dependence (AD) is characterized by corticostriatal impairments in individual brain areas such as the striatum. As yet however, complex brain network topology in AD and its association with disease progression are unknown. We applied graph theory to resting-state functional magnetic resonance imaging (RS-fMRI) to examine weighted global efficiency and local (clustering coefficient, degree and eigenvector centrality) network topology and the functional role of the striatum in 24 AD patients compared with 20 matched healthy controls (HCs), and their association with dependence characteristics. Graph analyses were performed based on Pearson's correlations between RS-fMRI time series, while correcting for age, gender and head motion. We found no significant group differences between AD patients and HCs in network topology. Notably, within the patient group, but not in HCs, the whole-brain network showed reduced average cluster coefficient with more severe alcohol use, whereas longer AD duration within the patient group was associated with a global decrease in efficiency, degree and clustering coefficient. Additionally, within four a-priori chosen bilateral striatal nodes, alcohol use severity was associated with lower clustering coefficient in the left caudate. Longer AD duration was associated with reduced clustering coefficient in caudate and putamen, and reduced degree in bilateral caudate, but with increased eigenvector centrality in left posterior putamen. Especially changes in global network topology and clustering coefficient in anterior striatum remained strikingly robust after exploratory variations in network weight. Our results show adverse effects of AD on overall network integration and possibly on striatal efficiency, putatively contributing to the increasing behavioral impairments seen in chronically addicted patients.

Altered anterior-posterior connectivity through the arcuate fasciculus in temporal lobe epilepsy.

How the interactions between cortices through a specific white matter pathway change during cognitive processing in patients with epilepsy remains unclear. Here, we used surface-based structural connectivity analysis to examine the change in structural connectivity with Broca's area/the right Broca's homologue in the lateral temporal and inferior parietal cortices through the arcuate fasciculus (AF) in 17 patients with left temporal lobe epilepsy (TLE) compared with 17 healthy controls. Then, we investigated its functional relevance to the changes in task-related responses and task-modulated functional connectivity with Broca's area/the right Broca's homologue during a semantic classification task of a single word. The structural connectivity through the AF pathway and task-modulated functional connectivity with Broca's area decreased in the left midtemporal cortex. Furthermore, task-related response decreased in the left mid temporal cortex that overlapped with the region showing a decrease in the structural connectivity. In contrast, the region showing an increase in the structural connectivity through the AF overlapped with the regions showing an increase in task-modulated functional connectivity in the left inferior parietal cortex. These structural and functional changes in the overlapping regions were correlated. The results suggest that the change in the structural connectivity through the left frontal-temporal AF pathway underlies the altered functional networks between the frontal and temporal cortices during the language-related processing in patients with left TLE. The left frontal-parietal AF pathway might be employed to connect anterior and posterior brain regions during language processing and compensate for the compromised left frontal-temporal AF pathway. Hum Brain Mapp 37:4425-4438, 2016. © 2016 Wiley Periodicals, Inc.

Resting-State Functional MR Imaging for Determining Language Laterality in Intractable Epilepsy.

Purpose To measure the accuracy of resting-state functional magnetic resonance (MR) imaging in determining hemispheric language dominance in patients with medically intractable focal epilepsies against the results of an intracarotid amobarbital procedure (IAP). Materials and Methods This study was approved by the institutional review board, and all subjects gave signed informed consent. Data in 23 patients with medically intractable focal epilepsy were retrospectively analyzed. All 23 patients were candidates for epilepsy surgery and underwent both IAP and resting-state functional MR imaging as part of presurgical evaluation. Language dominance was determined from functional MR imaging data by calculating a laterality index (LI) after using independent component analysis. The accuracy of this method was assessed against that of IAP by using a variety of thresholds. Sensitivity and specificity were calculated by using leave-one-out cross validation. Spatial maps of language components were qualitatively compared among each hemispheric language dominance group. Results Measurement of hemispheric language dominance with resting-state functional MR imaging was highly concordant with IAP results, with up to 96% (22 of 23) accuracy, 96% (22 of 23) sensitivity, and 96% (22 of 23) specificity. Composite language component maps in patients with typical language laterality consistently included classic language areas such as the inferior frontal gyrus, the posterior superior temporal gyrus, and the inferior parietal lobule, while those of patients with atypical language laterality also included non-classical language areas such as the superior and middle frontal gyri, the insula, and the occipital cortex. Conclusion Resting-state functional MR imaging can be used to measure language laterality in patients with medically intractable focal epilepsy. (©) RSNA, 2016 Online supplemental material is available for this article.

Using network dynamic fMRI for detection of epileptogenic foci.

BACKGROUND: Epilepsy is one of the most prevalent neurological disorders. It remains medically intractable for about one-third of patients with focal epilepsy, for whom precise localization of the epileptogenic zone responsible for seizure initiation may be critical for successful surgery. Existing fMRI literature points to widespread network disturbances in functional connectivity. Per previous scalp and intracranial EEG studies and consistent with excessive local synchronization during interictal discharges, we hypothesized that, relative to same regions in healthy controls, epileptogenic foci would exhibit less chaotic dynamics, identifiable via entropic analyses of resting state fMRI time series. METHODS: In order to first validate this hypothesis on a cohort of patients with known ground truth, here we test individuals with well-defined epileptogenic foci (left mesial temporal lobe epilepsy). We analyzed voxel-wise resting-state fMRI time-series using the autocorrelation function (ACF), an entropic measure of regulation and feedback, and performed follow-up seed-to-voxel functional connectivity analysis. Disruptions in connectivity of the region exhibiting abnormal dynamics were examined in relation to duration of epilepsy and patients' cognitive performance using a delayed verbal memory recall task. RESULTS: ACF analysis revealed constrained (less chaotic) functional dynamics in left temporal lobe epilepsy patients, primarily localized to ipsilateral temporal pole, proximal to presumed focal points. Autocorrelation decay rates differentiated, with 100 % accuracy, between patients and healthy controls on a subject-by-subject basis within a leave-one-subject out classification framework. Regions identified via ACF analysis formed a less efficient network in patients, as compared to controls. Constrained dynamics were linked with locally increased and long-range decreased connectivity that, in turn, correlated significantly with impaired memory (local left temporal connectivity) and epilepsy duration (left temporal - posterior cingulate cortex connectivity). CONCLUSIONS: Our current results suggest that data driven functional MRI methods that target network dynamics hold promise in providing clinically valuable tools for identification of epileptic regions.

Altered structural connectome in temporal lobe epilepsy.

PURPOSE: To study differences in the whole-brain structural connectomes of patients with left temporal lobe epilepsy (TLE) and healthy control subjects. MATERIALS AND METHODS: This study was approved by the institutional review board, and all individuals gave signed informed consent. Sixty-direction diffusion-tensor imaging and magnetization-prepared rapid acquisition gradient-echo (MP-RAGE) magnetic resonance imaging volumes were analyzed in 24 patients with left TLE and in 24 healthy control subjects. MP-RAGE volumes were segmented into 1015 regions of interest (ROIs) spanning the entire brain. Deterministic white matter tractography was performed after voxelwise tensor calculation. Weighted structural connectivity matrices were generated by using the pairwise density of connecting fibers between ROIs. Graph theoretical measures of connectivity networks were compared between groups by using linear models with permutation testing. RESULTS: Patients with TLE had 22%-45% reduced (P < .01) distant connectivity in the medial orbitofrontal cortex, temporal cortex, posterior cingulate cortex, and precuneus, compared with that in healthy subjects. However, local connectivity, as measured by means of network efficiency, was increased by 85%-270% (P < .01) in the medial and lateral frontal cortices, insular cortex, posterior cingulate cortex, precuneus, and occipital cortex in patients with TLE as compared with healthy subjects. CONCLUSION: This study suggests that TLE involves altered structural connectivity in a network that reaches beyond the temporal lobe, especially in the default mode network.

Sensor-Head Distance and Signal Strength in Whole-Head Magnetoencephalography: Report of 996 Patients With Epilepsy.

PURPOSE: Although the sensor-to-head distance is theoretically known to affect the signal strength in magnetoencephalography (MEG), these values have not been reported for a whole-head MEG system in a large population. We measured the distance and signal strength in 996 patients with epilepsy. METHODS: The MEG sensor array consisted of 102 measurement sites, each of which had two gradiometers and one magnetometer. The sensor-head distance was defined as the minimum distance between each site and a set of digitized scalp points. For the signal strength, we calculated the root-mean-square of the signal values in each sensor over a recording of 4 minutes. For analyses at the individual and sensor levels, these values were averaged over the sensors and patients, respectively. We evaluated the correlation between distance and signal strength at both individual and sensor levels. At the sensor level, we investigated regional differences in these measures. RESULTS: The individual-level analysis showed only a weak negative correlation between the sensor-head distance and the signal strength. The sensor-level analysis demonstrated a considerably negative correlation for both gradiometers and magnetometers. The sensor-head distances showed no significant differences between the regions, whereas the signal strength was higher in the temporal and occipital sensors than in the frontal and parietal sensors. CONCLUSIONS: Sensor-head distance was not a definitive factor for determining the magnitude of MEG signals in individuals. Yet, the distance is important for the signal strength at a sensor level. Regional differences in signal strength may need to be considered in the analysis and interpretation of MEG.

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.

Imaging and anesthesia protocol optimization in sedated clinical resting state fMRI.

BACKGROUND AND PURPOSE: The quality of resting-state functional MRI (rs-fMRI) under anesthesia is variable and there are no guidelines on optimal image acquisition or anesthesia protocol. We aim to identify the factors that may lead to compromised clinical rs-fMRI under anesthesia. MATERIALS AND METHODS: In this cross-sectional study, we analyzed clinical rs-fMRI data acquired under anesthesia from 2009-2023 at Massachusetts General Hospital. Independent component analysis driven resting state networks (RSN) of each patient were evaluated qualitatively and quantitatively and grouped as robust or weak. Overall networks were evaluated using the qualitative method, and motor and language networks were evaluated using the quantitative method. RSN robustness was analyzed in 4 outcome categories: overall, combined Motor-Language, individual motor, and language networks. Predictor variables included rs-fMRI acquisition parameters, anesthesia medications, underlying brain structural abnormalities, age, and sex. Logistic regression was used to examine the effect of the study variables on RSN robustness. RESULTS: Sixty-nine patients were identified. With qualitative assessment, 40 had robust and 29 had weak overall RSN. Quantitatively, 45 patients had robust, while 24 had weak Motor-Language networks. Among all the predictor variables, only sevoflurane significantly contributed to the outcomes, with sevoflurane administration reducing the odds of having robust RSN in overall (Odds Radio (OR)= 0.2, 95% Confidence Interval (CI) = [0.05;0.79], p = .02), Motor-Language (OR = 0.18, 95% CI = [0.04;0.80], p = .02) and individual motor (OR= 0.1, 95% CI = [0.02;0.64], p= .02) categories. Individual language network robustness was not associated with the tested predictor variables. CONCLUSIONS: Sevoflurane anesthesia may compromise the visibility of fMRI resting state networks, particularly impacting motor networks. This finding suggests that the type of anesthesia is a critical factor in rs-fMRI quality. We did not observe the association of the MR acquisition technique or underlying structural abnormality with the RSN robustness. ABBREVIATIONS: BOLD = Blood Oxygen Level-Dependent; ICA = Independent Component Analysis; Rs-fMRI = Resting-State Functional Magnetic Resonance Imaging; RSN = Resting-State Networks; SNR = Signal-to-Noise Ratio.

Decreased language laterality in tuberous sclerosis complex: a relationship between language dominance and tuber location as well as history of epilepsy.

Nearly 90% of patients with tuberous sclerosis complex (TSC) have epilepsy. Epilepsy surgery can be considered, which often requires a presurgical assessment of language lateralization. This is the first study to investigate language lateralization in TSC patients using magnetoencephalography. Fifteen patients performed a language task during magnetoencephalography recording. Cerebral generators of language-evoked fields (EF) were identified in each patient. Laterality indices (LI) were computed using magnetoencephalography data extracted from the inferior frontal as well as middle and superior temporal gyri from both hemispheres between 250 and 550 ms. Source analysis demonstrated a fusiform gyrus activation, followed by an activation located in the basal temporal language area and middle and superior temporal gyri responses, ending with an inferior frontal activation. Eleven patients (73.3%) had left-hemisphere language dominance, whereas four patients (26.7%) showed a bilateral language pattern, which was associated with a history of epilepsy and presence of tubers in language-related areas.