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1.
Cell Rep ; 28(10): 2554-2566.e7, 2019 Sep 03.
Artigo em Inglês | MEDLINE | ID: mdl-31484068

RESUMO

Optimizing direct electrical stimulation for the treatment of neurological disease remains difficult due to an incomplete understanding of its physical propagation through brain tissue. Here, we use network control theory to predict how stimulation spreads through white matter to influence spatially distributed dynamics. We test the theory's predictions using a unique dataset comprising diffusion weighted imaging and electrocorticography in epilepsy patients undergoing grid stimulation. We find statistically significant shared variance between the predicted activity state transitions and the observed activity state transitions. We then use an optimal control framework to posit testable hypotheses regarding which brain states and structural properties will efficiently improve memory encoding when stimulated. Our work quantifies the role that white matter architecture plays in guiding the dynamics of direct electrical stimulation and offers empirical support for the utility of network control theory in explaining the brain's response to stimulation.

2.
Neuroimage Clin ; 23: 101908, 2019.
Artigo em Inglês | MEDLINE | ID: mdl-31491812

RESUMO

Patients with drug-resistant focal epilepsy are often candidates for invasive surgical therapies. In these patients, it is necessary to accurately localize seizure generators to ensure seizure freedom following intervention. While intracranial electroencephalography (iEEG) is the gold standard for mapping networks for surgery, this approach requires inducing and recording seizures, which may cause patient morbidity. The goal of this study is to evaluate the utility of mapping interictal (non-seizure) iEEG networks to identify targets for surgical treatment. We analyze interictal iEEG recordings and neuroimaging from 27 focal epilepsy patients treated via surgical resection. We generate interictal functional networks by calculating pairwise correlation of iEEG signals across different frequency bands. Using image coregistration and segmentation, we identify electrodes falling within surgically resected tissue (i.e. the resection zone), and compute node-level and edge-level synchrony in relation to the resection zone. We further associate these metrics with post-surgical outcomes. Greater overlap between resected electrodes and highly synchronous electrodes is associated with favorable post-surgical outcomes. Additionally, good-outcome patients have significantly higher connectivity localized within the resection zone compared to those with poorer postoperative seizure control. This finding persists following normalization by a spatially-constrained null model. This study suggests that spatially-informed interictal network synchrony measures can distinguish between good and poor post-surgical outcomes. By capturing clinically-relevant information during interictal periods, our method may ultimately reduce the need for prolonged invasive implants and provide insights into the pathophysiology of an epileptic brain. We discuss next steps for translating these findings into a prospectively useful clinical tool.

3.
Epilepsy Behav ; : 106457, 2019 Aug 20.
Artigo em Inglês | MEDLINE | ID: mdl-31444029

RESUMO

Status epilepticus care and treatment are already being touched by the revolution in data science. New approaches designed to leverage the tremendous potential of "big data" in the clinical sphere are enabling researchers and clinicians to extract information from sources such as administrative claims data, the electronic medical health record, and continuous physiologic monitoring data streams. Algorithmic methods of data extraction also offer potential to fuse multimodal data (including text-based documentation, imaging data, and time-series data) to improve patient assessment and stratification beyond the manual capabilities of individual physicians. Still, the potential of data science to impact the diagnosis, treatment, and minute-to-minute care of patients with status epilepticus is only starting to be appreciated. In this brief review, we discuss how data science is impacting the field and draw examples from the following three main areas: (1) analysis of insurance claims from large administrative datasets to evaluate the impact of continuous electroencephalogram (EEG) monitoring on clinical outcomes; (2) natural language processing of the electronic health record to find, classify, and stratify patients for prognostication and treatment; and (3) real-time systems for data analysis, data reduction, and multimodal data fusion to guide therapy in real time. While early, it is our hope that these examples will stimulate investigators to leverage data science, computer science, and engineering methods to improve the care and outcome of patients with status epilepticus and other neurological disorders. This article is part of the Special Issue "Proceedings of the 7th London-Innsbruck Colloquium on Status Epilepticus and Acute Seizures".

4.
Brain ; 142(7): 1955-1972, 2019 Jul 01.
Artigo em Inglês | MEDLINE | ID: mdl-31099821

RESUMO

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.

5.
Neurology ; 92(20): e2329-e2338, 2019 May 14.
Artigo em Inglês | MEDLINE | ID: mdl-30971485

RESUMO

OBJECTIVE: To determine whether quantitative EEG (QEEG) features predict neurologic outcomes in children after cardiac arrest. METHODS: We performed a single-center prospective observational study of 87 consecutive children resuscitated and admitted to the pediatric intensive care unit after cardiac arrest. Full-array conventional EEG data were obtained as part of clinical management. We computed 8 QEEG features from 5-minute epochs every hour after return of circulation. We developed predictive models utilizing random forest classifiers trained on patient age and 8 QEEG features to predict outcome. The features included SD of each EEG channel, normalized band power in alpha, beta, theta, delta, and gamma wave frequencies, line length, and regularity function scores. We measured outcomes using Pediatric Cerebral Performance Category (PCPC) scores. We evaluated the models using 5-fold cross-validation and 1,000 bootstrap samples. RESULTS: The best performing model had a 5-fold cross-validation accuracy of 0.8 (0.88 area under the receiver operating characteristic curve). It had a positive predictive value of 0.79 and a sensitivity of 0.84 in predicting patients with favorable outcomes (PCPC score of 1-3). It had a negative predictive value of 0.8 and a specificity of 0.75 in predicting patients with unfavorable outcomes (PCPC score of 4-6). The model also identified the relative importance of each feature. Analyses using only frontal electrodes did not differ in prediction performance compared to analyses using all electrodes. CONCLUSIONS: QEEG features can standardize EEG interpretation and predict neurologic outcomes in children after cardiac arrest.

6.
Artigo em Inglês | MEDLINE | ID: mdl-30924499

RESUMO

BACKGROUND: Post-traumatic epilepsy (PTE) is a debilitating sequela of traumatic brain injury (TBI), occurring in up to 20% of severe cases. This entity is generally thought to be more difficult to treat with surgical intervention. OBJECTIVE: To detail our experience with the surgical treatment of PTE. METHODS: Patients with a history of head injury undergoing surgical treatment for epilepsy were retrospectively enrolled. Engel classification at the last follow-up was used to assess outcome of patients that underwent surgical resection of an epileptic focus. Reduction in seizure frequency was assessed for patients who underwent vagal nerve stimulator (VNS) or responsive neurostimulator (RNS) implantation. RESULTS: A total of 23 patients met inclusion criteria. Nineteen (82.6%) had mesial temporal sclerosis, 3 had lesional neocortical epilepsy (13.0%), and 1 had nonlesional neocortical epilepsy (4.3%). Fourteen patients (60.9%) underwent temporal lobectomy (TL), 2 underwent resection of a cortical focus (8.7%), and 7 underwent VNS implantation (30.4%). Three patients underwent RNS implantation after VNS failed to reduce seizure frequency more than 50%. In the patients treated with resection, 11 (68.8%) were Engel I, 3 (18.8%) were Engel II, and 2 (12.5%) were Engel III at follow-up. Average seizure frequency reduction in the VNS group was 30.6% ± 25.6%. RNS patients had reduction of seizure severity but seizure frequency was only reduced 9.6% ± 13.6%. CONCLUSION: Surgical outcomes of PTE patients treated with TL were similar to reported surgical outcomes of patients with nontraumatic epilepsy treated with TL. Patients who were not candidates for resection demonstrated variable response rates to VNS or RNS implantation.

7.
JAMA Neurol ; 76(6): 690-700, 2019 Jun 01.
Artigo em Inglês | MEDLINE | ID: mdl-30855662

RESUMO

Importance: A functional area associated with the piriform cortex, termed area tempestas, has been implicated in animal studies as having a crucial role in modulating seizures, but similar evidence is limited in humans. Objective: To assess whether removal of the piriform cortex is associated with postoperative seizure freedom in patients with temporal lobe epilepsy (TLE) as a proof-of-concept for the relevance of this area in human TLE. Design, Setting, and Participants: This cohort study used voxel-based morphometry and volumetry to assess differences in structural magnetic resonance imaging (MRI) scans in consecutive patients with TLE who underwent epilepsy surgery in a single center from January 1, 2005, through December 31, 2013. Participants underwent presurgical and postsurgical structural MRI and had at least 2 years of postoperative follow-up (median, 5 years; range, 2-11 years). Patients with MRI of insufficient quality were excluded. Findings were validated in 2 independent cohorts from tertiary epilepsy surgery centers. Study follow-up was completed on September 23, 2016, and data were analyzed from September 24, 2016, through April 24, 2018. Exposures: Standard anterior temporal lobe resection. Main Outcomes and Measures: Long-term postoperative seizure freedom. Results: In total, 107 patients with unilateral TLE (left-sided in 68; 63.6% women; median age, 37 years [interquartile range {IQR}, 30-45 years]) were included in the derivation cohort. Reduced postsurgical gray matter volumes were found in the ipsilateral piriform cortex in the postoperative seizure-free group (n = 46) compared with the non-seizure-free group (n = 61). A larger proportion of the piriform cortex was resected in the seizure-free compared with the non-seizure-free groups (median, 83% [IQR, 64%-91%] vs 52% [IQR, 32%-70%]; P < .001). The results were seen in left- and right-sided TLE and after adjusting for clinical variables, presurgical gray matter alterations, presurgical hippocampal volumes, and the proportion of white matter tract disconnection. Findings were externally validated in 2 independent cohorts (31 patients; left-sided TLE in 14; 54.8% women; median age, 41 years [IQR, 31-46 years]). The resected proportion of the piriform cortex was individually associated with seizure outcome after surgery (derivation cohort area under the curve, 0.80 [P < .001]; external validation cohorts area under the curve, 0.89 [P < .001]). Removal of at least half of the piriform cortex increased the odds of becoming seizure free by a factor of 16 (95% CI, 5-47; P < .001). Other mesiotemporal structures (ie, hippocampus, amygdala, and entorhinal cortex) and the overall resection volume were not associated with outcomes. Conclusions and Relevance: These results support the importance of resecting the piriform cortex in neurosurgical treatment of TLE and suggest that this area has a key role in seizure generation.

8.
J Neural Eng ; 16(2): 026016, 2019 Apr.
Artigo em Inglês | MEDLINE | ID: mdl-30560812

RESUMO

OBJECTIVE: Closed-loop implantable neural stimulators are an exciting treatment option for patients with medically refractory epilepsy, with a number of new devices in or nearing clinical trials. These devices must accurately detect a variety of seizure types in order to reliably deliver therapeutic stimulation. While effective, broadly-applicable seizure detection algorithms have recently been published, these methods are too computationally intensive to be directly deployed in an implantable device. We demonstrate a strategy that couples devices to cloud computing resources in order to implement complex seizure detection methods on an implantable device platform. APPROACH: We use a sensitive gating algorithm capable of running on-board a device to identify potential seizure epochs and transmit these epochs to a cloud-based analysis platform. A precise seizure detection algorithm is then applied to the candidate epochs, leveraging cloud computing resources for accurate seizure event detection. This seizure detection strategy was developed and tested on eleven human implanted device recordings generated using the NeuroVista Seizure Advisory System. MAIN RESULTS: The gating algorithm achieved high-sensitivity detection using a small feature set as input to a linear classifier, compatible with the computational capability of next-generation implantable devices. The cloud-based precision algorithm successfully identified all seizures transmitted by the gating algorithm while significantly reducing the false positive rate. Across all subjects, this joint approach detected 99% of seizures with a false positive rate of 0.03 h-1. SIGNIFICANCE: We present a novel framework for implementing computationally intensive algorithms on human data recorded from an implanted device. By using telemetry to intelligently access cloud-based computational resources, the next generation of neuro-implantable devices will leverage sophisticated algorithms with potential to greatly improve device performance and patient outcomes.

9.
Neurology ; 2018 Nov 30.
Artigo em Inglês | MEDLINE | ID: mdl-30504428

RESUMO

OBJECTIVE: To characterize continuous EEG (cEEG) use patterns in the critically ill and to determine the association with hospitalization outcomes for specific diagnoses. METHODS: We performed a retrospective cross-sectional study with National Inpatient Sample data from 2004 to 2013. We sampled hospitalized adult patients who received intensive care and then compared patients who underwent cEEG to those who did not. We considered diagnostic subgroups of seizure/status epilepticus, subarachnoid or intracerebral hemorrhage, and altered consciousness. Outcomes were in-hospital mortality, hospitalization cost, and length of stay. RESULTS: In total, 7,102,399 critically ill patients were identified, of whom 22,728 received cEEG. From 2004 to 2013, the proportion of patients who received cEEG increased from 0.06% (95% confidence interval [CI] 0.03%-0.09%) to 0.80% (95% CI 0.62%-0.98%). While the cEEG cohort appeared more ill, cEEG use was associated with reduced in-hospital mortality after adjustment for patient and hospital characteristics (odds ratio [OR] 0.83, 95% CI 0.75-0.93, p < 0.001). This finding held for the diagnoses of subarachnoid or intracerebral hemorrhage and for altered consciousness but not for the seizure/status epilepticus subgroup. Cost and length of hospitalization were increased for the cEEG cohort (OR 1.17 and OR 1.11, respectively, p < 0.001). CONCLUSIONS: There was a >10-fold increase in cEEG use from 2004 to 2013. However, this procedure may still be underused; cEEG was associated with lower in-hospital mortality but used for only 0.3% of the critically ill population. While administrative claims analysis supports the utility of cEEG for critically ill patients, our findings suggest variable benefit by diagnosis, and investigation with greater clinical detail is warranted.

10.
Neurology ; 2018 Dec 21.
Artigo em Inglês | MEDLINE | ID: mdl-30578373

RESUMO

OBJECTIVE: To determine the 30-day readmission rate after seizure-related discharge in a nationally representative sample, as well as patient, clinical, and hospital characteristics associated with readmission. METHODS: Retrospective cohort study of adults discharged alive from a nonelective hospitalization for epilepsy or seizure, sampled from the Healthcare Cost and Utilization Project's 2014 Nationwide Readmissions Database. Descriptive statistics and logistic regression models were built to quantify and characterize nonelective readmission within 30 days. RESULTS: A total of 139,800 admissions met inclusion criteria, of which 15,094 (10.8%) were readmitted within 30 days. Patient characteristics associated with readmission included comorbid disease burden (Elixhauser score 2: adjusted odds ratio [AOR] [95% confidence interval (CI)] 1.38 [1.21-1.57]; Elixhauser score 3: AOR 1.52 [1.34-1.73]; Elixhauser score >4: AOR 2.28 [2.01-2.58] as compared to 1) and participation in public insurance programs (Medicare: AOR 1.39 [1.26-1.54]; Medicaid: AOR 1.39 [1.26-1.54] as compared to private insurance). Adverse events (AOR 1.17 [1.05-1.30]) and prolonged length of stay, as well as nonroutine discharge (AOR 1.32 [1.23-1.42]), were also associated with increased adjusted odds of readmission. The most common primary reason for readmission was epilepsy or convulsion (17%). CONCLUSIONS: Patients hospitalized with seizure are frequently readmitted. While readmitted patients are more likely to have multiple medical comorbidities, our study demonstrated that inpatient adverse events were also significantly associated with readmission. The most common reason for readmission was seizure or epilepsy. Together, these 2 findings suggest that a proportion of readmissions are related to modifiable care process factors and may therefore be avoidable. Further study into understanding preventable drivers of readmission in this population presents an opportunity to improve patient outcomes and health.

11.
PLoS One ; 13(11): e0206137, 2018.
Artigo em Inglês | MEDLINE | ID: mdl-30383805

RESUMO

Intracranial electrodes are a vital component of implantable neurodevices, both for acute diagnostics and chronic treatment with open and closed-loop neuromodulation. Their performance is hampered by acute implantation trauma and chronic inflammation in response to implanted materials and mechanical mismatch between stiff synthetic electrodes and pulsating, natural soft host neural tissue. Flexible electronics based on thin polymer films patterned with microscale conductive features can help alleviate the mechanically induced trauma; however, this strategy alone does not mitigate inflammation at the device-tissue interface. In this study, we propose a biomimetic approach that integrates microscale extracellular matrix (ECM) coatings on microfabricated flexible subdural microelectrodes. Taking advantage of a high-throughput process employing micro-transfer molding and excimer laser micromachining, we fabricate multi-channel subdural microelectrodes primarily composed of ECM protein material and demonstrate that the electrochemical and mechanical properties match those of standard, uncoated controls. In vivo ECoG recordings in rodent brain confirm that the ECM microelectrode coatings and the protein interface do not alter signal fidelity. Astrogliotic, foreign body reaction to ECM coated devices is reduced, compared to uncoated controls, at 7 and 30 days, after subdural implantation in rat somatosensory cortex. We propose microfabricated, flexible, biomimetic electrodes as a new strategy to reduce inflammation at the device-tissue interface and improve the long-term stability of implantable subdural electrodes.

12.
Small ; 14(44): e1802563, 2018 Nov.
Artigo em Inglês | MEDLINE | ID: mdl-30286280

RESUMO

Drug delivery to a specific site in the body typically relies on the use of targeting agents that recognize a unique biomarker. Unfortunately, it is often difficult to identify unique molecular signatures that exist only at the site of interest. An alternative strategy is to deliver energy (e.g., light) to locally trigger release from a drug carrier; however, the use of this approach is limited because energy delivery to deep tissues is often impractical or invasive. In this work, radiofrequency-responsive superparamagnetic iron oxide nanoparticles (SPIONs) are used to trigger drug release from nanoscale vesicles. Because the body is inherently nonmagnetic, this approach allows for deep tissue targeting. To overcome the unfavorable meter-scale diffraction limit of SPION-compatible radiofrequency (RF) fields, a strong static gating field containing a sharp zero point is superimposed on the RF field. Only drug carriers that are at or near the zero point are susceptible to RF-triggered drug release, thereby localizing drug delivery with millimeter-scale resolution. This approach induces >40% drug release from thermally responsive doxorubicin-loaded liposomes within a 3.2 mm radius of the zero point with <10% release in the surrounding area, leading to a >2.5 therapeutic index in Huh 7 hepatocellular carcinoma cells.

13.
IEEE J Transl Eng Health Med ; 6: 2500112, 2018.
Artigo em Inglês | MEDLINE | ID: mdl-30310759

RESUMO

Brain stimulation has emerged as an effective treatment for a wide range of neurological and psychiatric diseases. Parkinson's disease, epilepsy, and essential tremor have FDA indications for electrical brain stimulation using intracranially implanted electrodes. Interfacing implantable brain devices with local and cloud computing resources have the potential to improve electrical stimulation efficacy, disease tracking, and management. Epilepsy, in particular, is a neurological disease that might benefit from the integration of brain implants with off-the-body computing for tracking disease and therapy. Recent clinical trials have demonstrated seizure forecasting, seizure detection, and therapeutic electrical stimulation in patients with drug-resistant focal epilepsy. In this paper, we describe a next-generation epilepsy management system that integrates local handheld and cloud-computing resources wirelessly coupled to an implanted device with embedded payloads (sensors, intracranial EEG telemetry, electrical stimulation, classifiers, and control policy implementation). The handheld device and cloud computing resources can provide a seamless interface between patients and physicians, and realtime intracranial EEG can be used to classify brain state (wake/sleep, preseizure, and seizure), implement control policies for electrical stimulation, and track patient health. This system creates a flexible platform in which low demand analytics requiring fast response times are embedded in the implanted device and more complex algorithms are implemented in offthebody local and distributed cloud computing environments. The system enables tracking and management of epileptic neural networks operating over time scales ranging from milliseconds to months.

14.
Brain ; 141(9): 2619-2630, 2018 Sep 01.
Artigo em Inglês | MEDLINE | ID: mdl-30101347

RESUMO

Accurate seizure prediction will transform epilepsy management by offering warnings to patients or triggering interventions. However, state-of-the-art algorithm design relies on accessing adequate long-term data. Crowd-sourcing ecosystems leverage quality data to enable cost-effective, rapid development of predictive algorithms. A crowd-sourcing ecosystem for seizure prediction is presented involving an international competition, a follow-up held-out data evaluation, and an online platform, Epilepsyecosystem.org, for yielding further improvements in prediction performance. Crowd-sourced algorithms were obtained via the 'Melbourne-University AES-MathWorks-NIH Seizure Prediction Challenge' conducted at kaggle.com. Long-term continuous intracranial electroencephalography (iEEG) data (442 days of recordings and 211 lead seizures per patient) from prediction-resistant patients who had the lowest seizure prediction performances from the NeuroVista Seizure Advisory System clinical trial were analysed. Contestants (646 individuals in 478 teams) from around the world developed algorithms to distinguish between 10-min inter-seizure versus pre-seizure data clips. Over 10 000 algorithms were submitted. The top algorithms as determined by using the contest data were evaluated on a much larger held-out dataset. The data and top algorithms are available online for further investigation and development. The top performing contest entry scored 0.81 area under the classification curve. The performance reduced by only 6.7% on held-out data. Many other teams also showed high prediction reproducibility. Pseudo-prospective evaluation demonstrated that many algorithms, when used alone or weighted by circadian information, performed better than the benchmarks, including an average increase in sensitivity of 1.9 times the original clinical trial sensitivity for matched time in warning. These results indicate that clinically-relevant seizure prediction is possible in a wider range of patients than previously thought possible. Moreover, different algorithms performed best for different patients, supporting the use of patient-specific algorithms and long-term monitoring. The crowd-sourcing ecosystem for seizure prediction will enable further worldwide community study of the data to yield greater improvements in prediction performance by way of competition, collaboration and synergism.10.1093/brain/awy210_video1awy210media15817489051001.

15.
Epilepsy Behav ; 86: 1-5, 2018 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-30032093

RESUMO

OBJECTIVE: Patients with poorly controlled seizures are at elevated risk of epilepsy-related morbidity and mortality. For patients with drug-resistant epilepsy that is focal at onset, epilepsy surgery is the most effective treatment available and offers a 50-80% cure rate. Yet, it is estimated that only 1% of patients with drug-resistant epilepsy undergo surgery in a timely fashion, and delays to surgery completion are considerable. The aim of this study was to increase availability and decrease delay of surgical evaluation at our epilepsy center for patients with drug-resistant epilepsy by removing process barriers. METHODS: For this quality improvement (QI) initiative, we convened a multidisciplinary team to construct a presurgical pathway process map and complete root cause analysis. This inquiry revealed that the current condition allowed patients to proceed through the pathway without centralized oversight. Therefore, we appointed an epilepsy surgery nurse manager, and under her direction, multiple additional process improvement interventions were applied. We then retrospectively compared preintervention (2014-2015) and postintervention (2016-2017) cohorts of patient undergoing the presurgical pathway. The improvement measures were patient throughput and pathway sojourn times. As a balancing measure, we considered the proportion of potentially eligible patients (epilepsy monitoring unit (EMU) admissions) who ultimately completed epilepsy surgery. RESULTS: Following our intervention, patient throughput was substantially increased for each stage of the presurgical pathway (32%-96% growth). However, patient sojourn times were not improved overall. No difference was observed in the proportion of possible candidates who ultimately completed epilepsy surgery. SIGNIFICANCE: Although process improvement expanded the number of patients who underwent epilepsy surgical evaluation, we experienced concurrent prolongation of the time from pathway initiation to completion. Ongoing improvement cycles will focus on newly identified residual sources of bottleneck and delay.

16.
J Neurophysiol ; 119(6): 2068-2081, 2018 Jun 01.
Artigo em Inglês | MEDLINE | ID: mdl-29488838

RESUMO

New devices that use targeted electrical stimulation to treat refractory localization-related epilepsy have shown great promise, although it is not well known which targets most effectively prevent the initiation and spread of seizures. To better understand how the brain transitions from healthy to seizing on a local scale, we induced focal epileptiform activity in the visual cortex of five anesthetized cats with local application of the GABAA blocker picrotoxin while simultaneously recording local field potentials on a high-resolution electrocorticography array and laminar depth probes. Epileptiform activity appeared in the form of isolated events, revealing a consistent temporal pattern of ictogenesis across animals with interictal events consistently preceding the appearance of seizures. Based on the number of spikes per event, there was a natural separation between seizures and shorter interictal events. Two distinct spatial regions were seen: an epileptic focus that grew in size as activity progressed, and an inhibitory surround that exhibited a distinct relationship with the focus both on the surface and in the depth of the cortex. Epileptiform activity in the cortical laminae was seen concomitant with activity on the surface. Focus spikes appeared earlier on electrodes deeper in the cortex, suggesting that deep cortical layers may be integral to recruiting healthy tissue into the epileptic network and could be a promising target for interventional devices. Our study may inform more effective therapies to prevent seizure generation and spread in localization-related epilepsies. NEW & NOTEWORTHY We induced local epileptiform activity and recorded continuous, high-resolution local field potentials from the surface and depth of the visual cortex in anesthetized cats. Our results reveal a consistent pattern of ictogenesis, characterize the spatial spread of the epileptic focus and its relationship with the inhibitory surround, and show that focus activity within events appears earliest in deeper cortical layers. These findings have potential implications for the monitoring and treatment of refractory epilepsy.

17.
Hum Brain Mapp ; 39(2): 851-865, 2018 02.
Artigo em Inglês | MEDLINE | ID: mdl-29159960

RESUMO

Medial temporal lobe (MTL) subregions play integral roles in memory function and are differentially affected in various neurological and psychiatric disorders. The ability to structurally and functionally characterize these subregions may be important to understanding MTL physiology and diagnosing diseases involving the MTL. In this study, we characterized network architecture of the MTL in healthy subjects (n = 31) using both resting state functional MRI and MTL-focused T2-weighted structural MRI at 7 tesla. Ten MTL subregions per hemisphere, including hippocampal subfields and cortical regions of the parahippocampal gyrus, were segmented for each subject using a multi-atlas algorithm. Both structural covariance matrices from correlations of subregion volumes across subjects, and functional connectivity matrices from correlations between subregion BOLD time series were generated. We found a moderate structural and strong functional inter-hemispheric symmetry. Several bilateral hippocampal subregions (CA1, dentate gyrus, and subiculum) emerged as functional network hubs. We also observed that the structural and functional networks naturally separated into two modules closely corresponding to (a) bilateral hippocampal formations, and (b) bilateral extra-hippocampal structures. Finally, we found a significant correlation in structural and functional connectivity (r = 0.25). Our findings represent a comprehensive analysis of network topology of the MTL at the subregion level. We share our data, methods, and findings as a reference for imaging methods and disease-based research.

18.
Lab Chip ; 18(3): 395-405, 2018 01 30.
Artigo em Inglês | MEDLINE | ID: mdl-29192299

RESUMO

New technologies that measure sparse molecular biomarkers from easily accessible bodily fluids (e.g. blood, urine, and saliva) are revolutionizing disease diagnostics and precision medicine. Microchip devices can measure more disease biomarkers with better sensitivity and specificity each year, but clinical interpretation of these biomarkers remains a challenge. Single biomarkers in 'liquid biopsy' often cannot accurately predict the state of a disease due to heterogeneity in phenotype and disease expression across individuals. To address this challenge, investigators are combining multiplexed measurements of different biomarkers that together define robust signatures for specific disease states. Machine learning is a useful tool to automatically discover and detect these signatures, especially as new technologies output increasing quantities of molecular data. In this paper, we review the state of the field of machine learning applied to molecular diagnostics and provide practical guidance to use this tool effectively and to avoid common pitfalls.


Assuntos
Biomarcadores/análise , Diagnóstico por Computador , Biópsia Líquida , Aprendizado de Máquina , Técnicas de Diagnóstico Molecular , Algoritmos , Análise por Conglomerados , Bases de Dados Factuais , Humanos
19.
Clin Neurophysiol ; 129(2): 360-367, 2017 Nov 16.
Artigo em Inglês | MEDLINE | ID: mdl-29288992

RESUMO

OBJECTIVE: Recent research suggests that high frequency intracranial EEG (iEEG) may improve localization of epileptic networks. This study aims to determine whether recording macroelectrode iEEG with higher sampling rates improves seizure localization in clinical practice. METHODS: 14 iEEG seizures from 10 patients recorded with >2000 Hz sampling rate were downsampled to four sampling rates: 100, 200, 500, 1000 Hz. In the 56 seizures, seizure onset time and location was marked by 5 independent, blinded EEG experts. RESULTS: When reading iEEG under clinical conditions, there was no consistent difference in time or localization of seizure onset or number of electrodes involved in the seizure onset zone with sampling rates varying from 100 to 1000 Hz. Stratification of patients by outcome did not improve with higher sampling rate. CONCLUSION: When utilizing standard clinical protocols, there was no benefit to acquiring iEEGs with sampling rate >100 Hz. Significant variability was noted in EEG marking both within and between individual expert EEG readers. SIGNIFICANCE: Although commercial equipment is capable of sampling much faster than 100 Hz, tools allowing visualization of subtle high frequency activity such as HFOs will be required to improve patient care. Quantitative methods may decrease reader variability, and potentially improve patient outcomes.

20.
ACS Nano ; 11(12): 12562-12572, 2017 12 26.
Artigo em Inglês | MEDLINE | ID: mdl-29178798

RESUMO

The chemistry that governs the dissolution of device-grade, monocrystalline silicon nanomembranes into benign end products by hydrolysis serves as the foundation for fully eco/biodegradable classes of high-performance electronics. This paper examines these processes in aqueous solutions with chemical compositions relevant to groundwater and biofluids. The results show that the presence of Si(OH)4 and proteins in these solutions can slow the rates of dissolution and that ion-specific effects associated with Ca2+ can significantly increase these rates. This information allows for effective use of silicon nanomembranes not only as active layers in eco/biodegradable electronics but also as water barriers capable of providing perfect encapsulation until their disappearance by dissolution. The time scales for this encapsulation can be controlled by introduction of dopants into the Si and by addition of oxide layers on the exposed surfaces.The former possibility also allows the doped silicon to serve as an electrical interface for measuring biopotentials, as demonstrated in fully bioresorbable platforms for in vivo neural recordings. This collection of findings is important for further engineering development of water-soluble classes of silicon electronics.

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