962 research outputs found
Seizure-onset mapping based on time-variant multivariate functional connectivity analysis of high-dimensional intracranial EEG : a Kalman filter approach
The visual interpretation of intracranial EEG (iEEG) is the standard method used in complex epilepsy surgery cases to map the regions of seizure onset targeted for resection. Still, visual iEEG analysis is labor-intensive and biased due to interpreter dependency. Multivariate parametric functional connectivity measures using adaptive autoregressive (AR) modeling of the iEEG signals based on the Kalman filter algorithm have been used successfully to localize the electrographic seizure onsets. Due to their high computational cost, these methods have been applied to a limited number of iEEG time-series (< 60). The aim of this study was to test two Kalman filter implementations, a well-known multivariate adaptive AR model (Arnold et al. 1998) and a simplified, computationally efficient derivation of it, for their potential application to connectivity analysis of high-dimensional (up to 192 channels) iEEG data. When used on simulated seizures together with a multivariate connectivity estimator, the partial directed coherence, the two AR models were compared for their ability to reconstitute the designed seizure signal connections from noisy data. Next, focal seizures from iEEG recordings (73-113 channels) in three patients rendered seizure-free after surgery were mapped with the outdegree, a graph-theory index of outward directed connectivity. Simulation results indicated high levels of mapping accuracy for the two models in the presence of low-to-moderate noise cross-correlation. Accordingly, both AR models correctly mapped the real seizure onset to the resection volume. This study supports the possibility of conducting fully data-driven multivariate connectivity estimations on high-dimensional iEEG datasets using the Kalman filter approach
Predicting Surgery Targets in Temporal Lobe Epilepsy through Structural Connectome Based Simulations
Temporal lobe epilepsy (TLE) is a prevalent neurological disorder resulting in disruptive seizures. In the case of drug resistant epilepsy resective surgery is often considered. This is a procedure hampered by unpredictable success rates, with many patients continuing to have seizures even after surgery. In this study we apply a computational model of epilepsy to patient specific structural connectivity derived from diffusion tensor imaging (DTI) of 22 individuals with left TLE and 39 healthy controls. We validate the model by examining patient-control differences in simulated seizure onset time and network location. We then investigate the potential of the model for surgery prediction by performing in silico surgical resections, removing nodes from patient networks and comparing seizure likelihood post-surgery to pre-surgery simulations. We find that, first, patients tend to transit from non-epileptic to epileptic states more often than controls in the model. Second, regions in the left hemisphere (particularly within temporal and subcortical regions) that are known to be involved in TLE are the most frequent starting points for seizures in patients in the model. In addition, our analysis also implicates regions in the contralateral and frontal locations which may play a role in seizure spreading or surgery resistance. Finally, the model predicts that patient-specific surgery (resection areas chosen on an individual, model-prompted, basis and not following a predefined procedure) may lead to better outcomes than the currently used routine clinical procedure. Taken together this work provides a first step towards patient specific computational modelling of epilepsy surgery in order to inform treatment strategies in individuals
Genetic and Neuroanatomical Support for Functional Brain Network Dynamics in Epilepsy
Focal epilepsy is a devastating neurological disorder that affects an
overwhelming number of patients worldwide, many of whom prove resistant to
medication. The efficacy of current innovative technologies for the treatment
of these patients has been stalled by the lack of accurate and effective
methods to fuse multimodal neuroimaging data to map anatomical targets driving
seizure dynamics. Here we propose a parsimonious model that explains how
large-scale anatomical networks and shared genetic constraints shape
inter-regional communication in focal epilepsy. In extensive ECoG recordings
acquired from a group of patients with medically refractory focal-onset
epilepsy, we find that ictal and preictal functional brain network dynamics can
be accurately predicted from features of brain anatomy and geometry, patterns
of white matter connectivity, and constraints complicit in patterns of gene
coexpression, all of which are conserved across healthy adult populations.
Moreover, we uncover evidence that markers of non-conserved architecture,
potentially driven by idiosyncratic pathology of single subjects, are most
prevalent in high frequency ictal dynamics and low frequency preictal dynamics.
Finally, we find that ictal dynamics are better predicted by white matter
features and more poorly predicted by geometry and genetic constraints than
preictal dynamics, suggesting that the functional brain network dynamics
manifest in seizures rely on - and may directly propagate along - underlying
white matter structure that is largely conserved across humans. Broadly, our
work offers insights into the generic architectural principles of the human
brain that impact seizure dynamics, and could be extended to further our
understanding, models, and predictions of subject-level pathology and response
to intervention
Imaging fast neural activity in the brain during epilepsy with electrical impedance tomography
Electrical impedance tomography (EIT) is a medical imaging technique which reconstructs images of the internal conductivity of an object using boundary measurements obtained by applying current through pairs of non-penetrating surface electrodes. EIT is able to image impedance changes which arise during neural activity at a high spatiotemporal resolution through the rat cerebral cortex and therefore represents a novel method for understanding neuronal network dynamics in epilepsy. Additionally, it holds therapeutic potential for improving the presurgical localisation of epileptogenic foci in individuals with drug-resistant epilepsy. This thesis was aimed at developing EIT for imaging epileptiform activity in vivo and assessing its potential for clinical use. Chapter 1 is a review of existing functional neuroimaging modalities, the principles of EIT and previous studies that have used EIT for imaging epileptic events. In Chapter 2, the safety of continuous current application to the rat cortical surface at 10-100 μA and 1725 Hz, parameters that are representative of fast neural EIT protocols, was verified by histological evaluation. Chapter 3 details the development of two acute rat models of focal epilepsy, the cortical and hippocampal epileptic afterdischarges models, for assessing the feasibility of imaging epileptiform activity with fast neural EIT using epicortical electrode arrays. In Chapter 4, EIT was used to image the propagation of ictal spike-and-wave activity through the cerebral cortex at a resolution of 2 ms and ≤300 µm. In order to enable imaging of epileptiform discharges in deeper subcortical structures, the optimal carrier frequency for current application was determined in Chapter 5. Results demonstrated that the maximal signal-to-noise ratio of fast neural impedance changes during ictal discharges is obtained at 1355 Hz. Finally, in Chapter 6, epileptiform activity in the hippocampus was imaged, with a localisation accuracy of ≤400 µm, using epicortical impedance measurements obtained at this optimised carrier frequency
GNAO1 encephalopathy: broadening the phenotype and evaluating treatment and outcome
OBJECTIVE:
To describe better the motor phenotype, molecular genetic features, and clinical course of GNAO1-related disease.
METHODS:
We reviewed clinical information, video recordings, and neuroimaging of a newly identified cohort of 7 patients with de novo missense and splice site GNAO1 mutations, detected by next-generation sequencing techniques.
RESULTS:
Patients first presented in early childhood (median age of presentation 10 months, range 0-48 months), with a wide range of clinical symptoms ranging from severe motor and cognitive impairment with marked choreoathetosis, self-injurious behavior, and epileptic encephalopathy to a milder phenotype, featuring moderate developmental delay associated with complex stereotypies, mainly facial dyskinesia and mild epilepsy. Hyperkinetic movements were often exacerbated by specific triggers, such as voluntary movement, intercurrent illnesses, emotion, and high ambient temperature, leading to hospital admissions. Most patients were resistant to drug intervention, although tetrabenazine was effective in partially controlling dyskinesia for 2/7 patients. Emergency deep brain stimulation (DBS) was life saving in 1 patient, resulting in immediate clinical benefit with complete cessation of violent hyperkinetic movements. Five patients had well-controlled epilepsy and 1 had drug-resistant seizures. Structural brain abnormalities, including mild cerebral atrophy and corpus callosum dysgenesis, were evident in 5 patients. One patient had a diffuse astrocytoma (WHO grade II), surgically removed at age 16.
CONCLUSIONS:
Our findings support the causative role of GNAO1 mutations in an expanded spectrum of early-onset epilepsy and movement disorders, frequently exacerbated by specific triggers and at times associated with self-injurious behavior. Tetrabenazine and DBS were the most useful treatments for dyskinesia
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Predicting neurosurgical outcomes in focal epilepsy patients using computational modelling
See Eissa and Schevon (doi:10.1093/aww332) for a scientific commentary on this article. Surgery can be a last resort for patients with intractable, medically refractory epilepsy. For many of these patients, however, there is substantial risk that the surgery will be ineffective. The prediction of who is likely to benefit from a surgical approach is crucial for being able to inform patients better, conduct principled prospective clinical trials, and ultimately tailor therapeutic approaches to these patients more effectively. Dynamical computational models, informed with patient data, can be used to make predictions and give mechanistic insight. In this study, we develop patient-specific dynamical network models of epileptogenic cortex. We infer the network connectivity matrix from non-seizure electrographic recordings of patients and use these connectivity matrices as the network structure in our model. The model simulates the dynamics of a bi-stable switch at every node in this network, meaning that every node starts in a background state, but has the ability to transit to a co-existing seizure state. Whether a transition happens in a node is partly determined by the stochastic nature of the input to the node, but also by the input the node receives from other connected nodes in the network. By conducting simulations with such a model, we can detect the average transition time for nodes in a given network, and therefore define nodes with a short transition time as highly epileptogenic. In a retrospective study, we found that in some patients the regions with high epileptogenicity in the model overlap with those identified clinically as the seizure onset zone. Moreover, it was found that the resection of these regions in the model reduces the overall likelihood of a seizure. Following removal of these regions in the model, we predicted surgical outcomes and compared these to actual patient outcomes. Our predictions were found to be 81.3% accurate on a dataset of 16 patients with intractable epilepsy. Intriguingly, in patients with unsuccessful outcomes, the proposed computational approach is able to suggest alternative resection sites. The model presented here gives mechanistic insight as to why surgery may be unsuccessful in some patients. This may aid clinicians in presurgical evaluation by providing a tool to explore various surgical options, offering complementary information to existing clinical techniques
Bi-allelic GAD1 variants cause a neonatal onset syndromic developmental and epileptic encephalopathy.
Developmental and epileptic encephalopathies are a heterogeneous group of early-onset epilepsy syndromes dramatically impairing neurodevelopment. Modern genomic technologies have revealed a number of monogenic origins and opened the door to therapeutic hopes. Here we describe a new syndromic developmental and epileptic encephalopathy caused by bi-allelic loss-of-function variants in GAD1, as presented by 11 patients from six independent consanguineous families. Seizure onset occurred in the first 2 months of life in all patients. All 10 patients, from whom early disease history was available, presented with seizure onset in the first month of life, mainly consisting of epileptic spasms or myoclonic seizures. Early EEG showed suppression-burst or pattern of burst attenuation or hypsarrhythmia if only recorded in the post-neonatal period. Eight patients had joint contractures and/or pes equinovarus. Seven patients presented a cleft palate and two also had an omphalocele, reproducing the phenotype of the knockout Gad1-/- mouse model. Four patients died before 4 years of age. GAD1 encodes the glutamate decarboxylase enzyme GAD67, a critical actor of the γ-aminobutyric acid (GABA) metabolism as it catalyses the decarboxylation of glutamic acid to form GABA. Our findings evoke a novel syndrome related to GAD67 deficiency, characterized by the unique association of developmental and epileptic encephalopathies, cleft palate, joint contractures and/or omphalocele
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