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1.
J Neural Eng ; 19(6)2022 Nov 24.
Artigo em Inglês | MEDLINE | ID: mdl-36356309

RESUMO

Objective. Speech decoding, one of the most intriguing brain-computer interface applications, opens up plentiful opportunities from rehabilitation of patients to direct and seamless communication between human species. Typical solutions rely on invasive recordings with a large number of distributed electrodes implanted through craniotomy. Here we explored the possibility of creating speech prosthesis in a minimally invasive setting with a small number of spatially segregated intracranial electrodes.Approach. We collected one hour of data (from two sessions) in two patients implanted with invasive electrodes. We then used only the contacts that pertained to a single stereotactic electroencephalographic (sEEG) shaft or an electrocorticographic (ECoG) stripe to decode neural activity into 26 words and one silence class. We employed a compact convolutional network-based architecture whose spatial and temporal filter weights allow for a physiologically plausible interpretation.Mainresults. We achieved on average 55% accuracy using only six channels of data recorded with a single minimally invasive sEEG electrode in the first patient and 70% accuracy using only eight channels of data recorded for a single ECoG strip in the second patient in classifying 26+1 overtly pronounced words. Our compact architecture did not require the use of pre-engineered features, learned fast and resulted in a stable, interpretable and physiologically meaningful decision rule successfully operating over a contiguous dataset collected during a different time interval than that used for training. Spatial characteristics of the pivotal neuronal populations corroborate with active and passive speech mapping results and exhibit the inverse space-frequency relationship characteristic of neural activity. Compared to other architectures our compact solution performed on par or better than those recently featured in neural speech decoding literature.Significance. We showcase the possibility of building a speech prosthesis with a small number of electrodes and based on a compact feature engineering free decoder derived from a small amount of training data.


Assuntos
Interfaces Cérebro-Computador , Eletrocorticografia , Humanos , Eletrocorticografia/métodos , Fala/fisiologia , Eletroencefalografia/métodos , Redes Neurais de Computação , Eletrodos
2.
Brain ; 145(11): 3901-3915, 2022 11 21.
Artigo em Inglês | MEDLINE | ID: mdl-36412516

RESUMO

Over 15 million epilepsy patients worldwide have drug-resistant epilepsy. Successful surgery is a standard of care treatment but can only be achieved through complete resection or disconnection of the epileptogenic zone, the brain region(s) where seizures originate. Surgical success rates vary between 20% and 80%, because no clinically validated biological markers of the epileptogenic zone exist. Localizing the epileptogenic zone is a costly and time-consuming process, which often requires days to weeks of intracranial EEG (iEEG) monitoring. Clinicians visually inspect iEEG data to identify abnormal activity on individual channels occurring immediately before seizures or spikes that occur interictally (i.e. between seizures). In the end, the clinical standard mainly relies on a small proportion of the iEEG data captured to assist in epileptogenic zone localization (minutes of seizure data versus days of recordings), missing opportunities to leverage these largely ignored interictal data to better diagnose and treat patients. IEEG offers a unique opportunity to observe epileptic cortical network dynamics but waiting for seizures increases patient risks associated with invasive monitoring. In this study, we aimed to leverage interictal iEEG data by developing a new network-based interictal iEEG marker of the epileptogenic zone. We hypothesized that when a patient is not clinically seizing, it is because the epileptogenic zone is inhibited by other regions. We developed an algorithm that identifies two groups of nodes from the interictal iEEG network: those that are continuously inhibiting a set of neighbouring nodes ('sources') and the inhibited nodes themselves ('sinks'). Specifically, patient-specific dynamical network models were estimated from minutes of iEEG and their connectivity properties revealed top sources and sinks in the network, with each node being quantified by source-sink metrics. We validated the algorithm in a retrospective analysis of 65 patients. The source-sink metrics identified epileptogenic regions with 73% accuracy and clinicians agreed with the algorithm in 93% of seizure-free patients. The algorithm was further validated by using the metrics of the annotated epileptogenic zone to predict surgical outcomes. The source-sink metrics predicted outcomes with an accuracy of 79% compared to an accuracy of 43% for clinicians' predictions (surgical success rate of this dataset). In failed outcomes, we identified brain regions with high metrics that were untreated. When compared with high frequency oscillations, the most commonly proposed interictal iEEG feature for epileptogenic zone localization, source-sink metrics outperformed in predictive power (by a factor of 1.2), suggesting they may be an interictal iEEG fingerprint of the epileptogenic zone.


Assuntos
Epilepsia , Convulsões , Humanos , Estudos Retrospectivos , Eletrocorticografia/métodos , Epilepsia/diagnóstico , Epilepsia/cirurgia , Biomarcadores
3.
J Neural Eng ; 19(6)2022 Nov 14.
Artigo em Inglês | MEDLINE | ID: mdl-36240727

RESUMO

Objective.We introduce extended Block-Term Tensor Regression (eBTTR), a novel regression method designed to account for the multilinear nature of human intracranial finger movement recordings.Approach.The proposed method relies on recursive Tucker decomposition combined with automatic component extraction.Main results.eBTTR outperforms state-of-the-art regression approaches, including multilinear and deep learning ones, in accurately predicting finger trajectories as well as unintentional finger coactivations.Significance.eBTTR rivals state-of-the-art approaches while being less computationally expensive which is an advantage when intracranial electrodes are implanted acutely, as part of the patient's presurgical workup, limiting time for decoder development and testing.


Assuntos
Interfaces Cérebro-Computador , Movimento , Humanos , Movimento/fisiologia , Dedos/fisiologia , Encéfalo/fisiologia , Eletrocorticografia/métodos
4.
Lancet Neurol ; 21(11): 982-993, 2022 11.
Artigo em Inglês | MEDLINE | ID: mdl-36270309

RESUMO

BACKGROUND: Intraoperative electrocorticography is used to tailor epilepsy surgery by analysing interictal spikes or spike patterns that can delineate epileptogenic tissue. High-frequency oscillations (HFOs) on intraoperative electrocorticography have been proposed as a new biomarker of epileptogenic tissue, with higher specificity than spikes. We prospectively tested the non-inferiority of HFO-guided tailoring of epilepsy surgery to spike-guided tailoring on seizure freedom at 1 year. METHODS: The HFO trial was a randomised, single-blind, adaptive non-inferiority trial at an epilepsy surgery centre (UMC Utrecht) in the Netherlands. We recruited children and adults (no age limits) who had been referred for intraoperative electrocorticography-tailored epilepsy surgery. Participants were randomly allocated (1:1) to either HFO-guided or spike-guided tailoring, using an online randomisation scheme with permuted blocks generated by an independent data manager, stratified by epilepsy type. Treatment allocation was masked to participants and clinicians who documented seizure outcome, but not to the study team or neurosurgeon. Ictiform spike patterns were always considered in surgical decision making. The primary endpoint was seizure outcome after 1 year (dichotomised as seizure freedom [defined as Engel 1A-B] vs seizure recurrence [Engel 1C-4]). We predefined a non-inferiority margin of 10% risk difference. Analysis was by intention to treat, with prespecified subgroup analyses by epilepsy type and for confounders. This completed trial is registered with the Dutch Trial Register, Toetsingonline ABR.NL44527.041.13, and ClinicalTrials.gov, NCT02207673. FINDINGS: Between Oct 10, 2014, and Jan 31, 2020, 78 individuals were enrolled to the study and randomly assigned (39 to HFO-guided tailoring and 39 to spike-guided tailoring). There was no loss to follow-up. Seizure freedom at 1 year occurred in 26 (67%) of 39 participants in the HFO-guided group and 35 (90%) of 39 in the spike-guided group (risk difference -23·5%, 90% CI -39·1 to -7·9; for the 48 patients with temporal lobe epilepsy, the risk difference was -25·5%, -45·1 to -6·0, and for the 30 patients with extratemporal lobe epilepsy it was -20·3%, -46·0 to 5·4). Pathology associated with poor prognosis was identified as a confounding factor, with an adjusted risk difference of -7·9% (90% CI -20·7 to 4·9; adjusted risk difference -12·5%, -31·0 to 5·9, for temporal lobe epilepsy and 5·8%, -7·7 to 19·5, for extratemporal lobe epilepsy). We recorded eight serious adverse events (five in the HFO-guided group and three in the spike-guided group) requiring hospitalisation. No patients died. INTERPRETATION: HFO-guided tailoring of epilepsy surgery was not non-inferior to spike-guided tailoring on intraoperative electrocorticography. After adjustment for confounders, HFOs show non-inferiority in extratemporal lobe epilepsy. This trial challenges the clinical value of HFOs as an epilepsy biomarker, especially in temporal lobe epilepsy. Further research is needed to establish whether HFO-guided intraoperative electrocorticography holds promise in extratemporal lobe epilepsy. FUNDING: UMCU Alexandre Suerman, EpilepsieNL, RMI Talent Fellowship, European Research Council, and MING Fund.


Assuntos
Epilepsias Parciais , Epilepsia do Lobo Temporal , Epilepsia , Adulto , Criança , Humanos , Eletrocorticografia , Método Simples-Cego , Países Baixos , Epilepsia/cirurgia , Convulsões/cirurgia , Epilepsias Parciais/cirurgia
5.
Biomed Microdevices ; 24(4): 31, 2022 09 17.
Artigo em Inglês | MEDLINE | ID: mdl-36138255

RESUMO

Electrocorticography signals, the intracranial recording of electrical signatures of the brain, are recorded by non-penetrating planar electrode arrays placed on the cortical surface. Flexible electrode arrays minimize the tissue damage upon implantation. This work shows the design and development of a 32-channel flexible microelectrode array to record electrocorticography signals from the rat's brain. The array was fabricated on a biocompatible flexible polyimide substrate. A titanium/gold layer was patterned as electrodes, and a thin polyimide layer was used for insulation. The fabricated microelectrode array was mounted on the exposed somatosensory cortex of the right hemisphere of a rat after craniotomy and incision of the dura. The signals were recorded using OpenBCI Cyton Daisy Biosensing Boards. The array faithfully recorded the baseline electrocorticography signals, the induced epileptic activities after applying a convulsant, and the recovered baseline signals after applying an antiepileptic drug. The signals recorded by such fabricated microelectrode array from anesthetized rats demonstrate its potential to monitor electrical signatures corresponding to epilepsy. Finally, the time-frequency analyses highlight the difference in spatiotemporal features of baseline and evoked epileptic discharges.


Assuntos
Eletrocorticografia , Titânio , Animais , Anticonvulsivantes , Convulsivantes , Eletrodos Implantados , Ouro , Microeletrodos , Ratos , Roedores
6.
Epileptic Disord ; 24(6): 1-6, 2022 12 01.
Artigo em Inglês | MEDLINE | ID: mdl-36153933

RESUMO

Objective: Immunity is increasingly implicated in the aetiology of certain types of epilepsy, however, the clinical and EEG features in such cases remain poorly defined. We present stereo-electroencephalography (SEEG) findings in patients who were thought to have autoantibody-mediated epilepsy on the basis of clinical improvement after administration of immunotherapy (IT). Methods: All patients undergoing SEEG implantation in our service were reviewed and those receiving immunotherapy, either before, during, or after SEEG evaluation, were identified. Response to immunotherapy was defined as greater than 50% seizure reduction. We compared the clinical features and SEEG findings between those who responded to immunotherapy and those who did not. Results: Sixty-two cases underwent SEEG evaluation. Of these, 11 received immunotherapy and three cases demonstrated a positive clinical benefit. The three responsive patients had multifocal seizure onset, repetitive spiking interictally and ictally, perisylvian semiology, seizure onset in the posterior perisylvian regions, and normal neuroimaging. Significance: Seronegative immunotherapy responders exist in epilepsy populations, therefore the diagnosis of autoimmune-associated epilepsy should be considered before proceeding to epilepsy surgery. Possible features of an electroclinical syndrome associated with autoimmunity may include multifocal seizure onset, perisylvian involvement, and normal neuroimaging.


Assuntos
Epilepsia Resistente a Medicamentos , Epilepsia , Epilepsia Resistente a Medicamentos/cirurgia , Epilepsia Resistente a Medicamentos/terapia , Eletrocorticografia , Eletroencefalografia/métodos , Epilepsia/diagnóstico , Humanos , Imunoterapia , Imageamento por Ressonância Magnética , Convulsões/cirurgia , Técnicas Estereotáxicas , Resultado do Tratamento
7.
Artigo em Inglês | MEDLINE | ID: mdl-36121939

RESUMO

Numerous state-of-the-art solutions for neural speech decoding and synthesis incorporate deep learning into the processing pipeline. These models are typically opaque and can require significant computational resources for training and execution. A deep learning architecture is presented that learns input bandpass filters that capture task-relevant spectral features directly from data. Incorporating such explainable feature extraction into the model furthers the goal of creating end-to-end architectures that enable automated subject-specific parameter tuning while yielding an interpretable result. The model is implemented using intracranial brain data collected during a speech task. Using raw, unprocessed timesamples, the model detects the presence of speech at every timesample in a causal manner, suitable for online application. Model performance is comparable or superior to existing approaches that require substantial signal preprocessing and the learned frequency bands were found to converge to ranges that are supported by previous studies.


Assuntos
Interfaces Cérebro-Computador , Aprendizado Profundo , Encéfalo , Eletrocorticografia , Humanos , Fala
8.
J Neural Eng ; 19(5)2022 09 23.
Artigo em Inglês | MEDLINE | ID: mdl-36084621

RESUMO

Objective.To determine the effect of epilepsy on intracranial electroencephalography (EEG) functional connectivity, and the ability of functional connectivity to localize the seizure onset zone (SOZ), controlling for spatial biases.Approach.We analyzed intracranial EEG data from patients with drug-resistant epilepsy admitted for pre-surgical planning. We calculated intracranial EEG functional networks and determined whether changes in functional connectivity lateralized the SOZ using a spatial subsampling method to control for spatial bias. We developed a 'spatial null model' to localize the SOZ electrode using only spatial sampling information, ignoring EEG data. We compared the performance of this spatial null model against models incorporating EEG functional connectivity and interictal spike rates.Main results.About 110 patients were included in the study, although the number of patients differed across analyses. Controlling for spatial sampling, the average connectivity was lower in the SOZ region relative to the same anatomic region in the contralateral hemisphere. A model using intra-hemispheric connectivity accurately lateralized the SOZ (average accuracy 75.5%). A spatial null model incorporating spatial sampling information alone achieved moderate accuracy in classifying SOZ electrodes (mean AUC = 0.70, 95% CI 0.63-0.77). A model incorporating intracranial EEG functional connectivity and spike rate data further outperformed this spatial null model (AUC 0.78,p= 0.002 compared to spatial null model). However, a model incorporating functional connectivity without spike rate data did not significantly outperform the null model (AUC 0.72,p= 0.38).Significance.Intracranial EEG functional connectivity is reduced in the SOZ region, and interictal data predict SOZ electrode localization and laterality, however a predictive model incorporating functional connectivity without interictal spike rates did not significantly outperform a spatial null model. We propose constructing a spatial null model to provide an estimate of the pre-implant hypothesis of the SOZ, and to serve as a benchmark for further machine learning algorithms in order to avoid overestimating model performance because of electrode sampling alone.


Assuntos
Eletrocorticografia , Epilepsia , Viés , Encéfalo , Eletrocorticografia/métodos , Eletroencefalografia/métodos , Epilepsia/diagnóstico , Epilepsia/cirurgia , Humanos , Convulsões
9.
Annu Int Conf IEEE Eng Med Biol Soc ; 2022: 3105-3110, 2022 07.
Artigo em Inglês | MEDLINE | ID: mdl-36086622

RESUMO

Virtual reality (VR) offers a robust platform for human behavioral neuroscience, granting unprecedented experimental control over every aspect of an immersive and interactive visual environment. VR experiments have already integrated non-invasive neural recording modalities such as EEG and functional MRI to explore the neural correlates of human behavior and cognition. Integration with implanted electrodes would enable significant increase in spatial and temporal resolution of recorded neural signals and the option of direct brain stimulation for neurofeedback. In this paper, we discuss the first such implementation of a VR platform with implanted electrocorticography (ECoG) and stereo-electroencephalography ( sEEG) electrodes in human, in-patient subjects. Noise analyses were performed to evaluate the effect of the VR headset on neural data collected in two VR-naive subjects, one child and one adult, including both ECOG and sEEG electrodes. Results demonstrate an increase in line noise power (57-63Hz) while wearing the VR headset that is mitigated effectively by common average referencing (CAR), and no significant change in the noise floor bandpower (125-240Hz). To our knowledge, this study represents first demonstrations of VR immersion during invasive neural recording with in-patient human subjects. Clinical Relevance- Immersive virtual reality tasks were well-tolerated and the quality of clinical neural signals preserved during VR immersion with two in-patient invasive neural recording subjects.


Assuntos
Eletrocorticografia , Realidade Virtual , Adulto , Criança , Eletrodos Implantados , Eletroencefalografia , Humanos , Imageamento por Ressonância Magnética
10.
Neurobiol Dis ; 174: 105863, 2022 Nov.
Artigo em Inglês | MEDLINE | ID: mdl-36165814

RESUMO

OBJECTIVES: Malformations of cortical development (MCDs) are common causes of drug-resistant epilepsy. The mechanisms underlying the associated epileptogenesis and ictogenesis remain poorly elucidated. EEG can help in understanding these mechanisms. We systematically reviewed studies reporting scalp or intracranial EEG features of MCDs to characterise interictal and seizure-onset EEG patterns across different MCD types. METHODS: We conducted a systematic review in accordance with PRISMA guidelines. MEDLINE, PubMed, and Cochrane databases were searched for studies describing interictal and seizure-onset EEG patterns in MCD patients. A classification framework was implemented to group EEG features into 20 predefined patterns, comprising nine interictal (five, scalp EEG; four, intracranial EEG) and 11 seizure-onset (five, scalp EEG; six, intracranial EEG) patterns. Logistic regression was used to estimate the odds ratios (OR) of each seizure-onset pattern being associated with specific MCD types. RESULTS: Our search yielded 1682 studies, of which 27 comprising 936 MCD patients were included. Of the nine interictal EEG patterns, five (three, scalp EEG; two, intracranial EEG) were detected in ≥2 MCD types, while four (rhythmic epileptiform discharges type 1 and type 2 on scalp EEG; repetitive bursting spikes and sporadic spikes on intracranial EEG) were seen only in focal cortical dysplasia (FCD). Of the 11 seizure-onset patterns, eight (three, scalp EEG; five, intracranial EEG) were found in ≥2 MCD types, whereas three were observed only in FCD (suppression on scalp EEG; delta brush on intracranial EEG) or tuberous sclerosis complex (TSC; focal fast wave on scalp EEG). Among scalp EEG seizure-onset patterns, paroxysmal fast activity (OR = 0.13; 95% CI: 0.03-0.53; p = 0.024) and repetitive epileptiform discharges (OR = 0.18; 95% CI: 0.05-0.61; p = 0.036) were less likely to occur in TSC than FCD. Among intracranial EEG seizure-onset patterns, low-voltage fast activity was more likely to be detected in heterotopia (OR = 19.3; 95% CI: 6.22-60.1; p < 0.001), polymicrogyria (OR = 6.70; 95% CI: 2.25-20.0; p = 0.004) and TSC (OR = 4.27; 95% CI: 1.88-9.70; p = 0.005) than FCD. SIGNIFICANCE: Different MCD types can share similar interictal or seizure-onset EEG patterns, reflecting common underlying biological mechanisms. However, selected EEG patterns appear to point to distinct MCD types, suggesting certain differences in their neuronal networks.


Assuntos
Malformações do Desenvolvimento Cortical , Convulsões , Humanos , Eletroencefalografia , Eletrocorticografia , Imageamento por Ressonância Magnética
11.
Comput Methods Programs Biomed ; 226: 107091, 2022 Nov.
Artigo em Inglês | MEDLINE | ID: mdl-36096023

RESUMO

BACKGROUND AND OBJECTIVE: Epilepsy is the second most prevalent neurological disorder of brain activity, affecting about seventy million people, or nearly 1% of the world population. Epileptic seizures prediction is extremely important for improving the epileptic patients' life. This paper proposed a novel method to predict seizures by detecting the critical transition of brain activities with intracranial EEG (iEEG) signals. METHODS: This article used three key measures of fluctuation, correlation and percolation to quantify pre-ictal states of epilepsy. Based on these measures, a ritical nucleus of iEEG signals was constructed and a composite index was introduced to detect the likelihood of impending seizures. In addition, we analyzed the dynamical mechanism of seizures at the tipping point from the perspective of spatial diffusion and temporal fluctuation. RESULTS: The empirical results supported that the seizures are self-initiated via a critical transition in pre-ictal state and showed that the proposed model can achieve a good prediction performance. The average accuracy, sensitivity, specificity and false-positive rate (FPR) attain 87.96%, 82.93%, 89.33% and 0.11/h respectively. The results also suggest that the temporal and spatial factors have strong synergistic effect on triggering seizures. For those seizures consistent with critical transition, the predictive performance was greatly improved with sensitivity up to 96.88%. CONCLUSIONS: This article proposed a critical nucleus model combined with spatial and temporal features of iEEG signals capable of seizure prediction. The proposed model brings insight from phase transition into epileptic iEEG signals analysis and quantifies the transition of the state to predict epileptic seizures with high accuracy.


Assuntos
Eletrocorticografia , Epilepsia , Humanos , Eletrocorticografia/métodos , Eletroencefalografia/métodos , Convulsões/diagnóstico , Epilepsia/diagnóstico , Núcleo Celular , Algoritmos
12.
Annu Int Conf IEEE Eng Med Biol Soc ; 2022: 2933-2936, 2022 07.
Artigo em Inglês | MEDLINE | ID: mdl-36086368

RESUMO

Seizure termination has received significantly less attention than initiation and propagation and consequently, remains a poorly understood phase of seizure evolution. Yet, its study may have a significant impact on the development of efficient interventional approaches, i.e., it may be critical for the design of treatments that induce or reproduce termination mechanisms that are triggered in self-terminating seizures. In this work, we aim to study temporal and spectral features of intracranial EEG (iEEG) during epileptic seizures to find time-frequency signatures that can predict the termination patterns. We propose a deep learning model for classification of multi channel iEEG epileptic seizure termination pattern into burst suppression and continuous bursting. We decompose the raw time series seizure data into time-frequency maps using Morlet Wavelet Transform. A Convolution Neural Network (CNN) is then trained on cross-patient time-frequency maps to classify the seizure termination patterns. For evaluation of classification performance, we compared the proposed method with k-Nearest Neighbour (k-NN). The CNN is shown to achieve an accuracy of 90 % and precision of 92 % as compared to 70% and 72% accuracy and precision achieved with the k-NN respectively. The proposed model is thus able to capture the temporal and spatial patterns which results in high performance of the classifier. This method of classification can be used to predict how a particular seizure will end and can potentially inform seizure management and treatment. Clinical relevance- This method establishes a model that can be used to classify seizure termination patterns with an accuracy of 90 % which can assist in better treatment of epilepsy patients.


Assuntos
Aprendizado Profundo , Epilepsia , Eletrocorticografia , Eletroencefalografia/métodos , Epilepsia/diagnóstico , Humanos , Convulsões/diagnóstico
13.
Annu Int Conf IEEE Eng Med Biol Soc ; 2022: 3159-3165, 2022 07.
Artigo em Inglês | MEDLINE | ID: mdl-36085770

RESUMO

We investigate a regularization framework for subject transfer learning in which we train an encoder and classifier to minimize classification loss, subject to a penalty measuring independence between the latent representation and the subject label. We introduce three notions of independence and corresponding penalty terms using mutual information or divergence as a proxy for independence. For each penalty term, we provide several concrete estimation algorithms, using analytic methods as well as neural critic functions. We propose a hands-off strategy for applying this diverse family of regularization schemes to a new dataset, which we call "Auto Transfer". We evaluate the performance of these individual regularization strategies under our AutoTransfer framework on EEG, EMG, and ECoG datasets, showing that these approaches can improve subject transfer learning for challenging real-world datasets.


Assuntos
Mãos , Aprendizagem , Algoritmos , Eletrocorticografia , Aprendizado de Máquina
14.
Annu Int Conf IEEE Eng Med Biol Soc ; 2022: 4139-4142, 2022 07.
Artigo em Inglês | MEDLINE | ID: mdl-36085655

RESUMO

Reliability, stability and biocompatibility of an im-plant are the keys to transferring a preclinical study into the re-ality of clinical applications for diagnostics and therapy of pa-tients. Amongst the smallest and most critical components of neuroprostheses are the neural interfaces to the tissue. These could be seen as the most functional and yet most sensitive parts to connect to and interact with the nervous system. Thin film systems in the submicro- to nanometers range with a high num-ber of channels record biological signals and excite nerves aspiring high spatial sensitivity at the scale of few neurons. The im-pairments of the technical material caused by the harsh environ-ment of the human body and potential damage to the tissue by the foreign body state the greatest obstacle to overcome. Here, we present an analysis on impact of acutely and chronically im-planted subdural electrocorticography (ECoG) recording arrays on the neural tissue and the accompanied material failure mechanisms of the thin film neural interfaces in vivo.


Assuntos
Tecido Nervoso , Platina , Eletrocorticografia , Humanos , Neurônios , Reprodutibilidade dos Testes
15.
Annu Int Conf IEEE Eng Med Biol Soc ; 2022: 4900-4903, 2022 07.
Artigo em Inglês | MEDLINE | ID: mdl-36085660

RESUMO

While the presence of spreading depolarization (SD) and associated spreading depression have been well studied and known to be associated with post-ischemic brain damage, the spatiotemporal spread of these events from the site of injury is not well understood. With the recent development of high-density micro-electrocorticographic (ECoG) electrode arrays, monitoring the spread of the depolarizing events and associated depression is possible. The goal of this work is to define the electrocorticographic features of SD and associated depression across the multichannel array and search for patterns in these features that emerge across both space and time. We present the spatial distribution of features found from chronic ECoG recordings acquired from awake behaving rats induced with a rodent model of stroke. SD events were detected with an unsupervised algorithm that searched for a stereotyped pattern in the first derivative of the ECoG. The algorithm yielded a 58% correct detection rate on average across four rats, and a 36% false positive rate. We defined key electrophysiological features and mapped them onto the physical brain regions using MATLAB, such as the peak-to-peak amplitude of each SD event, the width (or duration) of the SD event, direct current (DC) level, and average rate of decline in the signal baseline. We performed k-means clustering to the activity in this feature space which yielded three contiguous regions in physical space. The elbow optimization method was applied to a distortion metric and indicated that 3 clusters was optimal. These findings motivate us to conduct future studies that would verify whether these 3 clusters in electrode-space correspond to immunohistochemically defined regions of tissue health, namely, infarct, penumbra, and healthy tissue. Clinical Relevance- The extent and severity of damage that stroke ultimately causes is suspected to be related to the progression of spreading depolarization and associated depression. An understanding of how the features of these electrophysiological events progress across the brain and over time is an important step toward eventual development of closed-loop therapies which limit and minimize the long-term effects of stroke.


Assuntos
Eletrocorticografia , Acidente Vascular Cerebral , Animais , Encéfalo , Análise por Conglomerados , Ratos , Análise Espaço-Temporal , Acidente Vascular Cerebral/diagnóstico
16.
Annu Int Conf IEEE Eng Med Biol Soc ; 2022: 4892-4895, 2022 07.
Artigo em Inglês | MEDLINE | ID: mdl-36085684

RESUMO

Cortical mapping is widely employed to define the sensorimotor area and delineate the central sulcus (CS) during awake craniotomies. The approach involves the gold standard somatosensory evoked potentials (SSEPs) recorded with electrocorticogram (ECoG) strip electrodes. However, the evoked response can be misconstrued from the manual peak interpretation due to the poor spatial resolution of the strip electrode or when the electrode does not precisely cover the desired cortical area. This can lead to unintentional damage to the eloquent cortex. We present a soft real-time computer based visualization system that uses recorded SSEPs with a subdural grid to aid in cortical mapping. The neural data during electrical stimulation of the median nerve at 0.6Hz are picked up with a bio-amplifier at 2.4kHz. The stimulation artifact recorded from the bipolar electromyogram (EMG) is used as the stimulation onset. The ECoG data are assessed online with MATLAB Simulink to process and visualize the SSEPs waveform. The visualization system is programmed to display the SSEPs peak activation as a heat map on a 2D grid and projected onto a screen, showcasing the nature of the cortical activities over the contact surface area. Since the grid occupies a large cortical surface, the heatmap is able to delineate the central sulcus. The map can be viewed at any time point along the SSEP trace without the need for peak interpretation. With the goal to provide additional information during cortical mapping and facilitate interpretation of ECoG grid data, we believe that this visualization system will aid in rapid definition of the sensorimotor area during surgical planning. Clinical Relevance- This real-time visualization system can be used to delineate the central sulcus in a short time during awake craniotomies.


Assuntos
Eletrocorticografia , Córtex Sensório-Motor , Sistemas Computacionais , Eletrodos , Potenciais Somatossensoriais Evocados
17.
Annu Int Conf IEEE Eng Med Biol Soc ; 2022: 4896-4899, 2022 07.
Artigo em Inglês | MEDLINE | ID: mdl-36086062

RESUMO

Approximately 30% of patients with epilepsy do not respond to anti-epileptogenic drugs. Surgical removal of the epileptogenic zone (EZ), the brain regions where the seizures originate and spread, can be a possible therapy for these patients, but localizing the EZ is challenging due to a variety of clinical factors. High-frequency oscillations (HFOs) in intracranial electroencephalography (EEG) are a promising biomarker of the EZ, but it is currently unknown whether HFO rates and HFO morphology modulate as pathological brain networks evolve in a way that gives rise to seizures. To address this question, we assessed the temporal evolution of the duration of HFO events, amplitude of HFO events, and rates of HFOs per minute. HFO events were quantified using the 4AP in vivo rodent model of epilepsy, inducing seizures in two different brain areas. We found that the duration and amplitude of HFO events were significantly increased for the cortex model when compared to the hippocampus model. Additionally, the duration and amplitude increased significantly between baseline and pre-ictal HFOs in both models. On the other hand, the two models did not display a consistent increasing or decreasing trend in amplitude, duration or rate when comparing ictal and postictal intervals. Clinical Relevance- We assessed the amplitude, duration, and rate of HFOs in two acute in vivo rodent models of epilepsy. The significant modulation of HFO morphology from baseline to pre-ictal periods suggests that these features may be a robust biomarker for pathological tissue involved in epileptogenesis. Moreover, the differences in HFO morphology observed between cortex and hippocampus animal models possibly indicate that different structural network characteristics of the EZ cause this modulation. In all, we found that HFO features modulate significantly with the onset of seizures, further highlighting the need to consider of HFO morphology in EZ-localizing studies.


Assuntos
Eletroencefalografia , Epilepsia , Biomarcadores , Eletrocorticografia , Epilepsia/diagnóstico , Humanos , Convulsões
18.
Commun Biol ; 5(1): 909, 2022 09 05.
Artigo em Inglês | MEDLINE | ID: mdl-36064744

RESUMO

Non-invasive studies consider the initial neural stimulus response (SR) and repetition suppression (RS) - the decreased response to repeated sensory stimuli - as engaging the same neurons. That is, RS is a suppression of the SR. We challenge this conjecture using electrocorticographic (ECoG) recordings with high spatial resolution in ten patients listening to task-irrelevant trains of auditory stimuli. SR and RS were indexed by high-frequency activity (HFA) across temporal, parietal, and frontal cortices. HFASR and HFARS were temporally and spatially distinct, with HFARS emerging later than HFASR and showing only a limited spatial intersection with HFASR: most HFASR sites did not demonstrate HFARS, and HFARS was found where no HFASR could be recorded. ß activity was enhanced in HFARS compared to HFASR cortical sites. θ activity was enhanced in HFASR compared to HFARS sites. Furthermore, HFASR sites propagated information to HFARS sites via transient θ:ß phase-phase coupling. In contrast to predictive coding (PC) accounts our results indicate that HFASR and HFARS are functionally linked but have minimal spatial overlap. HFASR might enable stable and rapid perception of environmental stimuli across extended temporal intervals. In contrast HFARS might support efficient generation of an internal model based on stimulus history.


Assuntos
Percepção Auditiva , Lobo Temporal , Percepção Auditiva/fisiologia , Eletrocorticografia , Humanos , Lobo Temporal/fisiologia
19.
J Neural Eng ; 19(4)2022 08 24.
Artigo em Inglês | MEDLINE | ID: mdl-35931045

RESUMO

Objective.High-frequency oscillations (HFOs) are considered a biomarker of the epileptogenic zone in intracranial EEG recordings. However, automated HFO detectors confound true oscillations with spurious events caused by the presence of artifacts.Approach.We hypothesized that, unlike pseudo-HFOs with sharp transients or arbitrary shapes, real HFOs have a signal characteristic that can be represented using a small number of oscillatory bases. Based on this hypothesis using a sparse representation framework, this study introduces a new classification approach to distinguish true HFOs from the pseudo-events that mislead seizure onset zone (SOZ) localization. Moreover, we further classified the HFOs into ripples and fast ripples by introducing an adaptive reconstruction scheme using sparse representation. By visualizing the raw waveforms and time-frequency representation of events recorded from 16 patients, three experts labeled 6400 candidate events that passed an initial amplitude-threshold-based HFO detector. We formed a redundant analytical multiscale dictionary built from smooth oscillatory Gabor atoms and represented each event with orthogonal matching pursuit by using a small number of dictionary elements. We used the approximation error and residual signal at each iteration to extract features that can distinguish the HFOs from any type of artifact regardless of their corresponding source. We validated our model on sixteen subjects with thirty minutes of continuous interictal intracranial EEG recording from each.Main results.We showed that the accuracy of SOZ detection after applying our method was significantly improved. In particular, we achieved a 96.65% classification accuracy in labeled events and a 17.57% improvement in SOZ detection on continuous data. Our sparse representation framework can also distinguish between ripples and fast ripples.Significance.We show that by using a sparse representation approach we can remove the pseudo-HFOs from the pool of events and improve the reliability of detected HFOs in large data sets and minimize manual artifact elimination.


Assuntos
Eletrocorticografia , Eletroencefalografia , Artefatos , Eletroencefalografia/métodos , Humanos , Reprodutibilidade dos Testes , Convulsões/diagnóstico
20.
J Neural Eng ; 19(4)2022 08 23.
Artigo em Inglês | MEDLINE | ID: mdl-35931055

RESUMO

Objective. Implanted brain-computer interfaces (BCIs) employ neural signals to control a computer and may offer an alternative communication channel for people with locked-in syndrome (LIS). Promising results have been obtained using signals from the sensorimotor (SM) area. However, in earlier work on home-use of an electrocorticography (ECoG)-based BCI by people with LIS, we detected differences in ECoG-BCI performance, which were related to differences in the modulation of low frequency band (LFB) power in the SM area. For future clinical implementation of ECoG-BCIs, it will be crucial to determine whether reliable performance can be predicted before electrode implantation. To assess if non-invasive scalp-electroencephalography (EEG) could serve such prediction, we here investigated if EEG can detect the characteristics observed in the LFB modulation of ECoG signals.Approach. We included three participants with LIS of the earlier study, and a control group of 20 healthy participants. All participants performed a Rest task, and a Movement task involving actual (healthy) or attempted (LIS) hand movements, while their EEG signals were recorded.Main results.Data of the Rest task was used to determine signal-to-noise ratio, which showed a similar range for LIS and healthy participants. Using data of the Movement task, we selected seven EEG electrodes that showed a consistent movement-related decrease in beta power (13-30 Hz) across healthy participants. Within the EEG recordings of this subset of electrodes of two LIS participants, we recognized the phenomena reported earlier for the LFB in their ECoG recordings. Specifically, strong movement-related beta band suppression was observed in one, but not the other, LIS participant, and movement-related alpha band (8-12 Hz) suppression was practically absent in both. Results of the third LIS participant were inconclusive due to technical issues with the EEG recordings.Significance. Together, these findings support a potential role for scalp EEG in the presurgical assessment of ECoG-BCI candidates.


Assuntos
Interfaces Cérebro-Computador , Eletrocorticografia , Eletrocorticografia/métodos , Eletroencefalografia/métodos , Humanos , Movimento , Couro Cabeludo
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