Refractory seizures secondary to radiation-induced focal cortical dysplasia/neuronal gigantism: illustrative case

BACKGROUND

Focal cortical dysplasia is a structural cause of drug-resistant epilepsy commonly identified in childhood. In rare cases, radiation-induced injury has led to radiation-induced cortical dysplasia, also known as "focal neuronal gigantism."

OBSERVATIONS

The authors present a 53-year-old woman with recurrent status epilepticus events after she had radiation therapy and surgery for a left frontal meningioma several years prior. Imaging revealed findings consistent with radiation necrosis and possible recurrence. The patient's status epilepticus events required escalating therapies to manage. Scalp electroencephalography indicated that the seizure's origin was in the left hemisphere. A craniotomy was performed to remove the left frontal lesion, and histopathology was consistent with radiation-induced focal cortical dysplasia/neuronal gigantism. The patient's seizures ceased following the surgery, and she remains on maintenance antiseizure medications.

LESSONS

Radiation-induced focal cortical dysplasia/neuronal gigantism is an incredibly rare complication of therapy. However, it warrants consideration in the context of radiation necrosis and intractable epilepsy.

Time-evolving controllability of effective connectivity networks during seizure progression

Over one third of the estimated 3 million people with epilepsy in the United States are medication resistant. Responsive neurostimulation from chronically implanted electrodes provides a promising treatment alternative to resective surgery. However, determining optimal personalized stimulation parameters, including when and where to intervene to guarantee a positive patient outcome, is a major open challenge. Network neuroscience and control theory offer useful tools that may guide improvements in parameter selection for control of anomalous neural activity. Here we use a method to characterize dynamic controllability across consecutive effective connectivity (EC) networks based on regularized partial correlations between implanted electrodes during the onset, propagation, and termination regimes of 34 seizures. We estimate regularized partial correlation adjacency matrices from 1-s time windows of intracranial electrocorticography recordings using the Graphical Least Absolute Shrinkage and Selection Operator (GLASSO). Average and modal controllability metrics calculated from each resulting EC network track the time-varying controllability of the brain on an evolving landscape of conditionally dependent network interactions. We show that average controllability increases throughout a seizure and is negatively correlated with modal controllability throughout. Our results support the hypothesis that the energy required to drive the brain to a seizure-free state from an ictal state is smallest during seizure onset, yet we find that applying control energy at electrodes in the seizure onset zone may not always be energetically favorable. Our work suggests that a low-complexity model of time-evolving controllability may offer insights for developing and improving control strategies targeting seizure suppression.

Virtual resection predicts surgical outcome for drug-resistant epilepsy

Patients with drug-resistant epilepsy often require surgery to become seizure-free. While laser ablation and implantable stimulation devices have lowered the morbidity of these procedures, seizure-free rates have not dramatically improved, particularly for patients without focal lesions. This is in part because it is often unclear where to intervene in these cases. To address this clinical need, several research groups have published methods to map epileptic networks but applying them to improve patient care remains a challenge. In this study we advance clinical translation of these methods by: (i) presenting and sharing a robust pipeline to rigorously quantify the boundaries of the resection zone and determining which intracranial EEG electrodes lie within it; (ii) validating a brain network model on a retrospective cohort of 28 patients with drug-resistant epilepsy implanted with intracranial electrodes prior to surgical resection; and (iii) sharing all neuroimaging, annotated electrophysiology, and clinical metadata to facilitate future collaboration. Our network methods accurately forecast whether patients are likely to benefit from surgical intervention based on synchronizability of intracranial EEG (area under the receiver operating characteristic curve of 0.89) and provide novel information that traditional electrographic features do not. We further report that removing synchronizing brain regions is associated with improved clinical outcome, and postulate that sparing desynchronizing regions may further be beneficial. Our findings suggest that data-driven network-based methods can identify patients likely to benefit from resective or ablative therapy, and perhaps prevent invasive interventions in those unlikely to do so.

The sensitivity of network statistics to incomplete electrode sampling on intracranial EEG

Network neuroscience applied to epilepsy holds promise to map pathological networks, localize seizure generators, and inform targeted interventions to control seizures. However, incomplete sampling of the epileptic brain because of sparse placement of intracranial electrodes may affect model results. In this study, we evaluate the sensitivity of several published network measures to incomplete spatial sampling and propose an algorithm using network subsampling to determine confidence in model results. We retrospectively evaluated intracranial EEG data from 28 patients implanted with grid, strip, and depth electrodes during evaluation for epilepsy surgery. We recalculated global and local network metrics after randomly and systematically removing subsets of intracranial EEG electrode contacts. We found that sensitivity to incomplete sampling varied significantly across network metrics. This sensitivity was largely independent of whether seizure onset zone contacts were targeted or spared from removal. We present an algorithm using random subsampling to compute patient-specific confidence intervals for network localizations. Our findings highlight the difference in robustness between commonly used network metrics and provide tools to assess confidence in intracranial network localization. We present these techniques as an important step toward translating personalized network models of seizures into rigorous, quantitative approaches to invasive therapy.

Model-based design for seizure control by stimulation

OBJECTIVE

Current brain stimulation paradigms are largely empirical rather than theoretical. An opportunity exists to improve upon their modest effectiveness in closed-loop control strategies with the development of theoretically grounded, model-based designs.

APPROACH

Inspired by this need, here we couple experimental data and mathematical modeling with a control-theoretic strategy for seizure termination. We begin by exercising a dynamical systems approach to model seizures (n = 94) recorded using intracranial EEG (iEEG) from 21 patients with medication-resistant, localization-related epilepsy.

MAIN RESULTS

Although each patient's seizures displayed unique spatial and temporal patterns, their evolution can be parsimoniously characterized by the same model form. Idiosyncracies of the model can inform individualized intervention strategies, specifically in iEEG samples with well-localized seizure onset zones. Temporal fluctuations in the spatial profiles of the oscillatory modes show that seizure onset marks a transition into a regime in which the underlying system supports prolonged rhythmic and focal activity. Based on these observations, we propose a control-theoretic strategy that aims to stabilize ictal activity using static output feedback for linear time-invariant switching systems. Finally, we demonstrate in silico that our proposed strategy allows us to dampen the emerging focal oscillatory sources using only a small set of electrodes.

SIGNIFICANCE

Our integrative study informs the development of modulation and control algorithms for neurostimulation that could improve the effectiveness of implantable, closed-loop anti-epileptic devices.

Characterizing the role of the structural connectome in seizure dynamics

How does the human brain's structural scaffold give rise to its intricate functional dynamics? This is a central question in translational neuroscience that is particularly relevant to epilepsy, a disorder affecting over 50 million subjects worldwide. Treatment for medication-resistant focal epilepsy is often structural-through surgery or laser ablation-but structural targets, particularly in patients without clear lesions, are largely based on functional mapping via intracranial EEG. Unfortunately, the relationship between structural and functional connectivity in the seizing brain is poorly understood. In this study, we quantify structure-function coupling, specifically between white matter connections and intracranial EEG, across pre-ictal and ictal periods in 45 seizures from nine patients with unilateral drug-resistant focal epilepsy. We use high angular resolution diffusion imaging (HARDI) tractography to construct structural connectivity networks and correlate these networks with time-varying broadband and frequency-specific functional networks derived from coregistered intracranial EEG. Across all frequency bands, we find significant increases in structure-function coupling from pre-ictal to ictal periods. We demonstrate that short-range structural connections are primarily responsible for this increase in coupling. Finally, we find that spatiotemporal patterns of structure-function coupling are highly stereotyped for each patient. These results suggest that seizures harness the underlying structural connectome as they propagate. Mapping the relationship between structural and functional connectivity in epilepsy may inform new therapies to halt seizure spread, and pave the way for targeted patient-specific interventions.

Impaired transmissibility of atypical prions from genetic CJDG114V

Objective

To describe the clinicopathologic, molecular, and transmissible characteristics of genetic prion disease in a young man carrying the PRNP-G114V variant.

Methods

We performed genetic, histologic, and molecular studies, combined with in vivo transmission studies and in vitro replication studies, to characterize this genetic prion disease.

Results

A 24-year-old American man of Polish descent developed progressive dementia, aphasia, and ataxia, leading to his death 5 years later. Histologic features included widespread spongiform degeneration, gliosis, and infrequent PrP plaque-like deposits within the cerebellum and putamen, best classifying this as a Creutzfeldt-Jakob disease (CJD) subtype. Molecular typing of proteinase K-resistant PrP (resPrP^Sc) revealed a mixture of type 1 (∼21 kDa) and type 2 (∼19 kDa) conformations with only 2, rather than the usual 3, PrP^Sc glycoforms. Brain homogenates from the proband failed to transmit prion disease to transgenic Tg(HuPrP) mice that overexpress human PrP and are typically susceptible to sporadic and genetic forms of CJD. When subjected to protein misfolding cyclic amplification, the PrP^Sc type 2 (∼19 kDa) was selectively amplified.

Conclusions

The features of genetic CJD^G114V suggest that residue 114 within the highly conserved palindromic region (113-AGAAAAGA-120) plays an important role in prion conformation and propagation.

Lack of exacerbation of neurodegeneration in a double transgenic mouse model of mutant LRRK2 and tau

LRRK2 (leucine-rich repeat kinase) mutations constitute the most common cause of familial Parkinson's disease (PD). Microtubule-associated protein tau mutations cause a group of neurodegenerative diseases termed tauopathies. Genome-wide association studies show that, after α-synuclein, polymorphisms in the tau gene have the second strongest genetic association with PD. In a proportion of PD patients with LRRK2 mutations, and in several transgenic animal models of LRRK2, tau hyperphosphorylation and aggregation, rather than α-synuclein aggregation, are the most prominent neuropathologic findings. To further examine the relationship between LRRK2 and tau, we crossed LRRK2 R1441G BAC transgenic mice (Mus musculus) with tau P301S mutant transgenic mice and characterized their behavioral, neuropathological and biochemical phenotypes. We found that the combination of the two mutations does not increase tau hyperphosphorylation or aggregation nor does it exacerbate the behavioral and pathological deficits previously described in the tau P301S mice. The double-mutant mice had no shortening of lifespan and no worsening of motor or memory deficits. There was no increase in the aggregation of tau or α-synuclein. Dopaminergic neuron cell counts and striatal levels of dopamine and its metabolites were unaltered. There was no exacerbation of cell loss, microgliosis or astrogliosis in multiple brain regions. These results suggest that LRRK2 and tau do not interact to exacerbate behavioral, biochemical or pathological abnormalities in neurodegeneration and that LRRK2 and tau exert their pathogenic effects through independent mechanisms.

Inflammatory Flt3l is essential to mobilize dendritic cells and for T cell responses during Plasmodium infection

Innate sensing mechanisms trigger a variety of humoral and cellular events that are essential to adaptive immune responses. Here we describe an innate sensing pathway triggered by Plasmodium infection that regulates dendritic cell homeostasis and adaptive immunity through Flt3 ligand (Flt3l) release. Plasmodium-induced Flt3l release in mice requires Toll-like receptor (TLR) activation and type I interferon (IFN) production. We found that type I IFN supports the upregulation of xanthine dehydrogenase, which metabolizes the xanthine accumulating in infected erythrocytes to uric acid. Uric acid crystals trigger mast cells to release soluble Flt3l from a pre-synthesized membrane-associated precursor. During infection, Flt3l preferentially stimulates expansion of the CD8-α(+) dendritic cell subset or its BDCA3(+) human dendritic cell equivalent and has a substantial impact on the magnitude of T cell activation, mostly in the CD8(+) compartment. Our findings highlight a new mechanism that regulates dendritic cell homeostasis and T cell responses to infection.