Search bioRxiv⌕ Search

bioRxiv · 10.1101/747725

Node abnormality predicts seizure outcome and relates to long-term relapse after epilepsy surgery

Abstract

ObjectiveWe assessed pre-operative structural brain networks and clinical characteristics of patients with drug resistant temporal lobe epilepsy (TLE) to identify correlates of post-surgical seizure outcome at 1 year and seizure relapses up to 5 years.\n\nMethodsWe retrospectively examined data from 51 TLE patients who underwent anterior temporal lobe resection (ATLR) and 29 healthy controls. For each patient, using the pre-operative structural, diffusion, and post-operative structural MRI, we generated two networks: pre-surgery network and surgically-spared network. The pre-surgery network is the whole-brain network before surgery and the surgically-spared network is a subnetwork of the pre-surgery network which is expected to remain unaffected by surgery and hence present post-operatively. Standardising these networks with respect to controls, we determined the number of abnormal nodes before surgery and expected to remain after surgery. We incorporated these 2 abnormality measures and 13 commonly acquired clinical data from each patient in a robust machine learning framework to estimate patient-specific chances of seizures persisting after surgery.\n\nResultsPatients with more abnormal nodes had lower chance of seizure freedom at 1 year and even if seizure free at 1 year, were more likely to relapse within five years. In the surgically-spared networks of poor outcome patients, the number of abnormal nodes was greater and their locations more widespread than in good outcome patients. We achieved 0.84 {+/-} 0.06 AUC and 0.89 {+/-} 0.09 specificity in detecting unsuccessful seizure outcomes at 1-year. Moreover, the model-predicted likelihood of seizure relapse was significantly correlated with the grade of surgical outcome at year-one and associated with relapses up-to five years post-surgery.\n\nConclusionNode abnormality offers a personalised non-invasive marker, that can be combined with clinical data, to better estimate the chances of seizure freedom at 1 year, and subsequent relapse up to 5 years after ATLR.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Sinha, N., Wang, Y., Silva, N., Miserocchi, A., McEvoy, A. W., de Tisi, J., Vos, S. B., Winston, G. P., Duncan, J. S., Taylor, P. N.. 2019-09-01. Node abnormality predicts seizure outcome and relates to long-term relapse after epilepsy surgery. https://doi.org/10.1101/747725

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

Epidermal Langerhans Cells Act as Neuroimmune Regulators of Pain and Itch

Neuroimmune interactions in the skin coordinate immune defense and somatosensory perception, yet how immune cells shape sensory signaling remains poorly defined. Here, we show that Langerhans cells (LCs), epidermal antigen-presenting cells, function as local immune-derived amplifiers of cutaneous sensory signaling. Anatomical and transcriptomic analyses reveal preferential LC association with non-peptidergic sensory neurons. LC activation increases neuronal and behavioral responses to thermal, mechanical, and pruritic stimuli without directly exciting sensory neurons, whereas LC inhibition or ablation selectively reduces pruritogen-evoked itch. We identified semaphorin 7A as a candidate LC-expressed mediator. Semaphorin 7A sensitized cultured sensory neurons to chloroquine and histamine, and neutralizing semaphorin 7A abolished the increase in chloroquine-evoked scratching produced by LC activation. Its receptor PlexinC1 was expressed by most MrgprA3 pruriceptors and blocking PlexinC1 reduced semaphorin 7A-evoked neuronal responses. Together, these findings identify epidermal LCs as local immune-derived amplifiers that regulate the responsiveness of cutaneous sensory circuits and identify semaphorin 7A as a mediator of LC-induced itch potentiation.

neuroscience↗

Feasible closed-loop stimulation for neural latent dynamics control

Modern neuroscience benefits from powerful computational and experimental methods for studying neural populations, including latent dynamics models that predict behavior with few dimensions. But despite their explanatory utility, latent factors are seldom targeted explicitly with experimental perturbations, leaving causal relationships to behavior and inferred dynamical landscapes untested. A promising solution is closed-loop, multi-channel optogenetic control, which, when paired with model predictions, could steer neural trajectories in real time--a feat impossible for traditional, hand-designed stimulation. However, because closed-loop latent control has not been demonstrated in vivo, the hardware and software requirements remain unclear--particularly the necessary stimulation dimensionality and how limited resources such as implant volume and bandwidth should be allocated between stimulation and recording capabilities. Preliminary experiments would thus help guide ongoing technological development. As large-scale exploration of these design spaces in vivo is prohibitively expensive, we conducted a high-throughput simulation survey using biophysically grounded spiking neural networks. We found that low-dimensional actuation is sufficient: in the reference configuration, ~20% of the stimulation channels achieved ~85% of peak control performance, far fewer than the number of neurons. Furthermore, allocating channels roughly equally between stimulation and recording optimized behavioral control, whereas pairing many electrodes with a single light source--the prevailing practice in experimental labs--performed poorly. Even without heavily optimizing modeling or control, we obtained >60% tracking error reduction and >90% evoked behavior accuracy on average for optimal channel counts. These results held across extensive parameter variations, suggesting that closed-loop latent control is feasible with accessible channel counts and establishing design principles for future tool development and in vivo experiments.

neuroscience↗

Identification of Kinome Biomarkers and Therapeutic Rescue in cGMP-Linked Retinal Degeneration

Inherited retinal degenerations, such as Retinitis Pigmentosa (RP) and Leber's congenital amaurosis (LCA), are major causes of progressive and irreversible vision loss. The large number of genes associated with these disorders makes their clinical and molecular characterization difficult, while systemic biomarkers for disease progression or treatment response remain limited. These challenges were addressed by targeting cGMP-associated mechanisms shared by RP and LCA and integrating systemic biomarker profiling with retinal outcomes. Kinase activity analysis (kinome analysis) in peripheral blood mononuclear cells distinguished healthy and diseased states and captured therapy-associated signatures in rd10 mice treated with LP-CN03, a liposomal formulation of the cGMP analog CN03. This first systemic analysis in a therapeutically responsive animal model highlights kinome analysis as a potential translational readout. Given shared cGMP signaling in RP and AIPL1-associated LCA, LP-CN03's therapeutic potential was further evaluated in a new CRISPR-Cas9-generated Aipl1-/- mouse model. Treatment with LP-CN03 preserved retinal structure and function, as shown by electroretinography, longitudinal optical coherence tomography, fundus imaging, optomotor testing, and photoreceptor-specific gene expression. Together, these findings provide independent in vivo evidence for the neuroprotective effect of LP-CN03 in both cGMP-related RP and LCA, also highlighting the translational value of kinome profiling.

neuroscience↗