A new era in electroencephalographic monitoring? Subscalp devices for ultra-long-term recordings.

Inaccurate subjective seizure counting poses treatment and diagnostic challenges and thus suboptimal quality in epilepsy management. The limitations of existing hospital- and home-based monitoring solutions are motivating the development of minimally invasive, subscalp, implantable electroencephalography (EEG) systems with accompanying cloud-based software. This new generation of ultra-long-term brain monitoring systems is setting expectations for a sea change in the field of clinical epilepsy. From definitive diagnoses and reliable seizure logs to treatment optimization and presurgical seizure foci localization, the clinical need for continuous monitoring of brain electrophysiological activity in epilepsy patients is evident. This paper presents the converging solutions developed independently by researchers and organizations working at the forefront of next generation EEG monitoring. The immediate value of these devices is discussed as well as the potential drivers and hurdles to adoption. Additionally, this paper discusses what the expected value of ultra-long-term EEG data might be in the future with respect to alarms for especially focal seizures, seizure forecasting, and treatment personalization.

Thalamic volume reduction in drug-naive patients with new-onset genetic generalized epilepsy.

OBJECTIVE: Patients with genetic generalized epilepsy (GGE) have subtle morphologic abnormalities of the brain revealed with magnetic resonance imaging (MRI), particularly in the thalamus. However, it is unclear whether morphologic abnormalities of the brain in GGE are a consequence of repeated seizures over the duration of the disease, or are a consequence of treatment with antiepileptic drugs (AEDs), or are independent of these factors. Therefore, we measured brain morphometry in a cohort of AED-naive patients with GGE at disease onset. We hypothesize that drug-naive patients at disease onset have gray matter changes compared to age-matched healthy controls. METHODS: We performed quantitative measures of gray matter volume in the thalamus, putamen, caudate, pallidum, hippocampus, precuneus, prefrontal cortex, precentral cortex, and cingulate in 29 AED-naive patients with new-onset GGE and compared them to 32 age-matched healthy controls. We subsequently compared the shape of any brain structures found to differ in gray matter volume between the groups. RESULTS: The thalamus was the only structure to show reduced gray matter volume in AED-naive patients with new-onset GGE compared to healthy controls. Shape analysis revealed that the thalamus showed deflation, which was not uniformly distributed, but particularly affected a circumferential strip involving anterior, superior, posterior, and inferior regions with sparing of medial and lateral regions. SIGNIFICANCE: Structural abnormalities in the thalamus are present at the initial onset of GGE in AED-naive patients, suggesting that thalamic structural abnormality is an intrinsic feature of GGE and not a consequence of AEDs or disease duration.

White Matter Connectivity of the Thalamus Delineates the Functional Architecture of Competing Thalamocortical Systems.

There is an increasing awareness of the involvement of thalamic connectivity on higher level cortical functioning in the human brain. This is reflected by the influence of thalamic stimulation on cortical activity and behavior as well as apparently cortical lesion syndromes occurring as a function of small thalamic insults. Here, we attempt to noninvasively test the correspondence of structural and functional connectivity of the human thalamus using diffusion-weighted and resting-state functional MRI. Using a large sample of 102 adults, we apply tensor independent component analysis to diffusion MRI tractography data to blindly parcellate bilateral thalamus according to diffusion tractography-defined structural connectivity. Using resting-state functional MRI collected in the same subjects, we show that the resulting structurally defined thalamic regions map to spatially distinct, and anatomically predictable, whole-brain functional networks in the same subjects. Although there was significant variability in the functional connectivity patterns, the resulting 51 structural and functional patterns could broadly be reduced to a subset of 7 similar core network types. These networks were distinct from typical cortical resting-state networks. Importantly, these networks were distributed across the brain and, in a subset, map extremely well to known thalamocortico-basal-ganglial loops.

Thalamotemporal alteration and postoperative seizures in temporal lobe epilepsy.

OBJECTIVE: There are competing explanations for persistent postoperative seizures after temporal lobe surgery. One is that 1 or more particular subtypes of mesial temporal lobe epilepsy (mTLE) exist that are particularly resistant to surgery. We sought to identify a common brain structural and connectivity alteration in patients with persistent postoperative seizures using preoperative quantitative magnetic resonance imaging and diffusion tensor imaging (DTI). METHODS: We performed a series of studies in 87 patients with mTLE (47 subsequently rendered seizure free, 40 who continued to experience postoperative seizures) and 80 healthy controls. We investigated the relationship between imaging variables and postoperative seizure outcome. All patients had unilateral temporal lobe seizure onset, had ipsilateral hippocampal sclerosis as the only brain lesion, and underwent amygdalohippocampectomy. RESULTS: Quantitative imaging factors found not to be significantly associated with persistent seizures were volumes of ipsilateral and contralateral mesial temporal lobe structures, generalized brain atrophy, and extent of resection. There were nonsignificant trends for larger amygdala and entorhinal resections to be associated with improved outcome. However, patients with persistent seizures had significant atrophy of bilateral dorsomedial and pulvinar thalamic regions, and significant alterations of DTI-derived thalamotemporal probabilistic paths bilaterally relative to those patients rendered seizure free and controls, even when corrected for extent of mesial temporal lobe resection. INTERPRETATION: Patients with bihemispheric alterations of thalamotemporal structural networks may represent a subtype of mTLE that is resistant to temporal lobe surgery. Increasingly sensitive multimodal imaging techniques should endeavor to transform these group-based findings to individualize prediction of patient outcomes.

Investigation of glutamine and GABA levels in patients with idiopathic generalized epilepsy using MEGAPRESS.

PURPOSE: Idiopathic generalized epilepsies (IGE) comprise a group of clinical syndromes associated with spike wave discharges, putatively linked to alterations in neurotransmission. The purpose of this study was to investigate whether patients with IGE have altered glutamine and γ-aminobutyric acid (GABA) levels indicative of altered excitatory and inhibitory neurotransmission in frontal regions. MATERIALS AND METHODS: Single-voxel MEGA-edited PRESS magnetic resonance imaging (MRI) spectra were acquired from a 30-mL voxel in the dorsolateral prefrontal cortex in 13 patients with IGE (8 female) and 16 controls (9 female) at 3T. Metabolite concentrations were derived using LCModel. Differences between groups were investigated using an unpaired t-test. RESULTS: Patients with IGE were found to have significantly higher glutamine than controls (P = 0.02). GABA levels were also elevated in patients with IGE (P = 0.03). CONCLUSION: Patients with IGE have increased frontal glutamine and GABA compared with controls. Since glutamine has been suggested to act as a surrogate for metabolically active glutamate, it may represent a marker for excitatory neurotransmission.

Thalamotemporal impairment in temporal lobe epilepsy: a combined MRI analysis of structure, integrity, and connectivity.

OBJECTIVE: Thalamic abnormality in temporal lobe epilepsy (TLE) is well known from imaging studies, but evidence is lacking regarding connectivity profiles of the thalamus and their involvement in the disease process. We used a novel multisequence magnetic resonance imaging (MRI) protocol to elucidate the relationship between mesial temporal and thalamic pathology in TLE. METHODS: For 23 patients with TLE and 23 healthy controls, we performed T1 -weighted (for analysis of tissue structure), diffusion tensor imaging (tissue connectivity), and T1 and T2 relaxation (tissue integrity) MRI across the whole brain. We used connectivity-based segmentation to determine connectivity patterns of thalamus to ipsilateral cortical regions (occipital, parietal, prefrontal, postcentral, precentral, and temporal). We subsequently determined volumes, mean tractography streamlines, and mean T1 and T2 relaxometry values for each thalamic segment preferentially connecting to a given cortical region, and of the hippocampus and entorhinal cortex. RESULTS: As expected, patients had significant volume reduction and increased T2 relaxation time in ipsilateral hippocampus and entorhinal cortex. There was bilateral volume loss, mean streamline reduction, and T2 increase of the thalamic segment preferentially connected to temporal lobe, corresponding to anterior, dorsomedial, and pulvinar thalamic regions, with no evidence of significant change in any other thalamic segments. Left and right thalamotemporal segment volume and T2 were significantly correlated with volume and T2 of ipsilateral (epileptogenic), but not contralateral (nonepileptogenic), mesial temporal structures. SIGNIFICANCE: These convergent and robust data indicate that thalamic abnormality in TLE is restricted to the area of the thalamus that is preferentially connected to the epileptogenic temporal lobe. The degree of thalamic pathology is related to the extent of mesial temporal lobe damage in TLE.

Abnormal thalamocortical structural and functional connectivity in juvenile myoclonic epilepsy.

Juvenile myoclonic epilepsy is the most common idiopathic generalized epilepsy, characterized by frequent myoclonic jerks, generalized tonic-clonic seizures and, less commonly, absences. Neuropsychological and, less consistently, anatomical studies have indicated frontal lobe dysfunction in the disease. Given its presumed thalamo-cortical basis, we investigated thalamo-cortical structural connectivity, as measured by diffusion tensor imaging, in a cohort of 28 participants with juvenile myoclonic epilepsy and detected changes in an anterior thalamo-cortical bundle compared with healthy control subjects. We then investigated task-modulated functional connectivity from the anterior thalamic region identified using functional magnetic resonance imaging in a task consistently shown to be impaired in this group, phonemic verbal fluency. We demonstrate an alteration in task-modulated connectivity in a region of frontal cortex directly connected to the thalamus via the same anatomical bundle, and overlapping with the supplementary motor area. Further, we show that the degree of abnormal connectivity is related to disease severity in those with active seizures. By integrating methods examining structural and effective interregional connectivity, these results provide convincing evidence for abnormalities in a specific thalamo-cortical circuit, with reduced structural and task-induced functional connectivity, which may underlie the functional abnormalities in this idiopathic epilepsy.

Characterising the dynamics of EEG waveforms as the path through parameter space of a neural mass model: application to epilepsy seizure evolution.

In this paper we propose that the dynamic evolution of EEG activity during epileptic seizures may be characterised as a path through parameter space of a neural mass model, reflecting gradual changes in underlying physiological mechanisms. Previous theoretical studies have shown how boundaries in parameter space of the model (so-called bifurcations) correspond to transitions in EEG waveforms between apparently normal, spike and wave and subsequently poly-spike and wave activity. In the present manuscript, we develop a multi-objective genetic algorithm that can estimate parameters of an underlying model from clinical data recordings. A standard approach to this problem is to transform both clinical data and model output into the frequency domain and then choose parameters that minimise the difference in their respective power spectra. Instead in the present manuscript, we estimate parameters in the time domain, their choice being determined according to the best fit obtained between the model output and specific features of the observed EEG waveform. This results in an approximate path through the bifurcation plane of the model obtained from clinical data. We present comparisons of such paths through parameter space from separate seizures from an individual subject, as well as between different subjects. Differences in the path reflect subtleties of variation in the dynamics of EEG, which at present appear indistinguishable using standard clinical techniques.

Segmentation of the thalamus in MRI based on T1 and T2.

Reliable identification of thalamic nuclei is required to improve positioning of electrodes in Deep Brain Stimulation (DBS), and to allow the role of individual thalamic nuclei in health and disease to be fully investigated. In this work, a previously proposed method for identifying sub-regions within the thalamus based on differences in their T1 and T2 values is explored in detail. The effect on the segmentation of T1 and T2 dependence weighted against priors for spatial position and extent was investigated. When T1 and T2 dependence was highly weighted, good distinction between identified regions was obtained in T1/T2 feature-space, but no contiguous anatomically distinct regions were identified within the thalamus. Incorporating spatial priors was necessary to ensure anatomically distinct regions were defined. Optimal values for segmentation parameters were obtained by assessing performance on a 'synthetic thalamus'. Using these optimum input parameters, within- and between-subjects reproducibility was assessed. Good reproducibility was obtained when six regions were specified to be identified in the thalamus. The six regions identified were similar in the majority of the normal subject group. However, intriguingly these regions were different from those obtained in the same subjects using a well-known connectivity-based segmentation technique. This method shows promise to identify intrathalamic structures on the basis of T1 and T2 signal. A comprehensive characterisation of thalamic nuclei may require a fully multi-modal approach.

Clustering probabilistic tractograms using independent component analysis applied to the thalamus.

The connectivity information contained in diffusion tensor imaging (DTI) has previously been used to parcellate cortical and subcortical regions based on their connectivity profiles. The aim of the current study is to investigate the utility of a novel approach to connectivity based parcellation of the thalamus using probabilistic tractography and independent component analysis (ICA). We use ICA to identify spatially coherent tractograms as well as their underlying seed regions, in a single step. We compare this to seed-based tractography results and to an established and reliable approach to parcellating the thalamus based on the dominant cortical connection from each thalamic voxel (Behrens et al., 2003a,b). The ICA approach identifies thalamo-cortical pathways that correspond to known anatomical connections, as well as parcellating the underlying thalamus in a spatially similar way to the connectivity based parcellation. We believe that the use of such a multivariate method to interpret the complex datasets created by probabilistic tractography may be better suited than other approaches to parcellating brain regions.

Reproducibility of thalamic segmentation based on probabilistic tractography.

Reliable identification of thalamic nuclei is required to improve targeting of electrodes used in Deep Brain Stimulation (DBS), and for exploring the role of thalamus in health and disease. A previously described method using probabilistic tractography to segment the thalamus based on connections to cortical target regions was implemented. Both within- and between-subject reproducibility were quantitatively assessed by the overlap of the resulting segmentations; the effect of two different numbers of target regions (6 and 31) on reproducibility of the segmentation results was also investigated. Very high reproducibility was observed when a single dataset was processed multiple times using different starting conditions. Thalamic segmentation was also very reproducible when multiple datasets from the same subject were processed using six cortical target regions. Within-subject reproducibility was reduced when the number of target regions was increased, particularly in medial and posterior regions of the thalamus. A large degree of overlap in segmentation results from different subjects was obtained, particularly in thalamic regions classified as connecting to frontal, parietal, temporal and pre-central cortical target regions.

Transitions to spike-wave oscillations and epileptic dynamics in a human cortico-thalamic mean-field model.

In this paper we present a detailed theoretical analysis of the onset of spike-wave activity in a model of human electroencephalogram (EEG) activity, relating this to clinical recordings from patients with absence seizures. We present a complete explanation of the transition from inter-ictal activity to spike and wave using a combination of bifurcation theory, numerical continuation and techniques for detecting the occurrence of inflection points in systems of delay differential equations (DDEs). We demonstrate that the initial transition to oscillatory behaviour occurs as a result of a Hopf bifurcation, whereas the addition of spikes arises as a result of an inflection point of the vector field. Strikingly these findings are consistent with EEG data recorded from patients with absence seizures and we present a discussion of the clinical significance of these results, suggesting potential new techniques for detection and anticipation of seizures.

Derivation and analysis of an ordinary differential equation mean-field model for studying clinically recorded epilepsy dynamics.

In this paper we describe how an ordinary differential equation model of corticothalamic interactions may be obtained from a more general system of delay differential equations. We demonstrate that transitions to epileptic dynamics via changes in system parameters are qualitatively the same as in the original model with delay, as well as demonstrating that the onset of epileptic activity may arise due to regions of bistability. Hence, the model presents in one unique framework, two competing theories for the genesis of epileptiform activity. Similarities between model transitions and clinical data are presented and we argue that statistics obtained from, and a parameter estimation of this model may be a potential means of classifying and predicting the onset and offset of seizure activity.

Onset of polyspike complexes in a mean-field model of human electroencephalography and its application to absence epilepsy.

In this paper, we introduce a modification of a mean-field model used to describe the brain's electrical activity as recorded via electroencephalography (EEG). The focus of the present study is to understand the mechanisms giving rise to the dynamics observed during absence epilepsy, one of the classical generalized syndromes. A systematic study of the data from a number of different subjects with absence epilepsy demonstrates a wide variety of dynamical phenomena in the recorded EEG. In addition to the classical spike and wave activity, there may be polyspike and wave, wave spike or even no discernible spike-wave onset during seizure events. The model we introduce is able to capture all of these different phenomena and we describe the bifurcations giving rise to these different types of seizure activity. We argue that such a model may provide a useful clinical tool for classifying different subclasses of absence epilepsy.