Dataset Information

Cross-scale effects of neural interactions during human neocortical seizure activity.

ABSTRACT: Small-scale neuronal networks may impose widespread effects on large network dynamics. To unravel this relationship, we analyzed eight multiscale recordings of spontaneous seizures from four patients with epilepsy. During seizures, multiunit spike activity organizes into a submillimeter-sized wavefront, and this activity correlates significantly with low-frequency rhythms from electrocorticographic recordings across a 10-cm-sized neocortical network. Notably, this correlation effect is specific to the ictal wavefront and is absent interictally or from action potential activity outside the wavefront territory. To examine the multiscale interactions, we created a model using a multiscale, nonlinear system and found evidence for a dual role for feedforward inhibition in seizures: while inhibition at the wavefront fails, allowing seizure propagation, feedforward inhibition of the surrounding centimeter-scale networks is activated via long-range excitatory connections. Bifurcation analysis revealed that distinct dynamical pathways for seizure termination depend on the surrounding inhibition strength. Using our model, we found that the mesoscopic, local wavefront acts as the forcing term of the ictal process, while the macroscopic, centimeter-sized network modulates the oscillatory seizure activity.

SUBMITTER: Eissa TL

PROVIDER: S-EPMC5635869 | biostudies-literature | 2017 Oct

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Cross-scale effects of neural interactions during human neocortical seizure activity.

Eissa Tahra L TL Dijkstra Koen K Brune Christoph C Emerson Ronald G RG van Putten Michel J A M MJAM Goodman Robert R RR McKhann Guy M GM Schevon Catherine A CA van Drongelen Wim W van Gils Stephan A SA

Proceedings of the National Academy of Sciences of the United States of America 20170918 40

Small-scale neuronal networks may impose widespread effects on large network dynamics. To unravel this relationship, we analyzed eight multiscale recordings of spontaneous seizures from four patients with epilepsy. During seizures, multiunit spike activity organizes into a submillimeter-sized wavefront, and this activity correlates significantly with low-frequency rhythms from electrocorticographic recordings across a 10-cm-sized neocortical network. Notably, this correlation effect is specific ...[more]

PMID: 28923948

Similar Datasets

Project description:Approximately 30% of patients with epilepsy do not achieve adequate seizure control through current anti-seizure drugs and treatment methods. Therefore, a critical need exists to efficiently screen anti-seizure drugs to enhance our ability to tailor treatment protocols and improve patient outcomes. The zebrafish pentylenetetrazol (PTZ) seizure model has become an increasingly popular screening paradigm for novel anti-seizure compounds. However, previous research using this model was variable due to differing experimental methods. Here, we present a method that was optimized to improve reliability and reproducibility in our laboratory using this PTZ model to develop a more robust screening of anti-seizure drugs comparing behavior and neural activity. Our behavior assay, spanning 90 min using 10 mM PTZ on 7 days post fertilization zebrafish, provides a broad window to observe anti-seizure drug efficacy. To compare our method with previously published data, we tested carbamazepine, lamotrigine, and topiramate, which have been tested in previous PTZ zebrafish assays. In addition, we assessed the candidate anti-seizure compound GS967, which has not been previously tested in the zebrafish seizure model. We examined the efficacy of anti-seizure drugs by acute administration concurrent with PTZ application and by pretreatment prior to exposure with PTZ. Pretreatment permitted us to examine potential neuroprotection and determine whether treatment time affects anti-seizure drugs' responses. As independent validation of anti-seizure drugs' effects, we evaluated whether the anti-seizure drug efficacy in the behavioral assay correlated with neural activity measurements, using electroencephalogram (EEG) and calcium signaling using GCaMP. There was no significant difference in the reduction of PTZ-induced seizure behavior activity between the pretreatment groups and acute treatment groups. Acute treatment with anti-seizure drugs in the EEG and GCaMP assays from 15 to 30 min post-anti-seizure drug exposure revealed consistent results between behavioral, EEG, and GCaMP assays for two of the three anti-seizure drugs. Lamotrigine only reduced neural activity (EEG and GCaMP assays). Carbamazepine, topiramate, and GS967 reduced activity in all three assays. The findings show that EEG and GCaMP assays largely correlate with the behavior findings, helping us connect physiological and behavior responses to anti-seizure drug and better assess anti-seizure drug efficacy.

Project description:The apparent unpredictability of epileptic seizures has a major impact in the quality of life of people with pharmacologically resistant seizures. Here, we present initial results and a proof-of-concept of how focal seizures can be predicted early in advance based on intracortical signals recorded from small neocortical patches away from identified seizure onset areas. We show that machine learning algorithms can discriminate between interictal and preictal periods based on multiunit activity (i.e. thresholded action potential counts) and multi-frequency band local field potentials recorded via 4 X 4 mm2 microelectrode arrays. Microelectrode arrays were implanted in 5 patients undergoing neuromonitoring for resective surgery. Post-implant analysis revealed arrays were outside the seizure onset areas. Preictal periods were defined as the 1-hour period leading to a seizure. A 5-minute gap between the preictal period and the putative seizure onset was enforced to account for potential errors in the determination of actual seizure onset times. We used extreme gradient boosting and long short-term memory networks for prediction. Prediction accuracy based on the area under the receiver operating characteristic curves reached 90% for at least one feature type in each patient. Importantly, successful prediction could be achieved based exclusively on multiunit activity. This result indicates that preictal activity in the recorded neocortical patches involved not only subthreshold postsynaptic potentials, perhaps driven by the distal seizure onset areas, but also neuronal spiking in distal recurrent neocortical networks. Beyond the commonly identified seizure onset areas, our findings point to the engagement of large-scale neuronal networks in the neural dynamics building up toward a seizure. Our initial results obtained on currently available human intracortical microelectrode array recordings warrant new studies on larger datasets, and open new perspectives for seizure prediction and control by emphasizing the contribution of multiscale neural signals in large-scale neuronal networks.

Project description:Interpersonal interaction is the essence of human social behavior. However, conventional neuroimaging techniques have tended to focus on social cognition in single individuals rather than on dyads or groups. As a result, relatively little is understood about the neural events that underlie face-to-face interaction. We resolved some of the technical obstacles inherent in studying interaction using a novel imaging modality and aimed to identify neural mechanisms engaged both within and across brains in an ecologically valid instance of interpersonal competition. Functional near-infrared spectroscopy was utilized to simultaneously measure hemodynamic signals representing neural activity in pairs of subjects playing poker against each other (human-human condition) or against computer opponents (human-computer condition). Previous fMRI findings concerning single subjects confirm that neural areas recruited during social cognition paradigms are individually sensitive to human-human and human-computer conditions. However, it is not known whether face-to-face interactions between opponents can extend these findings. We hypothesize distributed effects due to live processing and specific variations in across-brain coherence not observable in single-subject paradigms. Angular gyrus (AG), a component of the temporal-parietal junction (TPJ) previously found to be sensitive to socially relevant cues, was selected as a seed to measure within-brain functional connectivity. Increased connectivity was confirmed between AG and bilateral dorsolateral prefrontal cortex (dlPFC) as well as a complex including the left subcentral area (SCA) and somatosensory cortex (SS) during interaction with a human opponent. These distributed findings were supported by contrast measures that indicated increased activity at the left dlPFC and frontopolar area that partially overlapped with the region showing increased functional connectivity with AG. Across-brain analyses of neural coherence between the players revealed synchrony between dlPFC and supramarginal gyrus (SMG) and SS in addition to synchrony between AG and the fusiform gyrus (FG) and SMG. These findings present the first evidence of a frontal-parietal neural complex including the TPJ, dlPFC, SCA, SS, and FG that is more active during human-to-human social cognition both within brains (functional connectivity) and across brains (across-brain coherence), supporting a model of functional integration of socially and strategically relevant information during live face-to-face competitive behaviors.

Dataset Information

Cross-scale effects of neural interactions during human neocortical seizure activity.

Publications

Cross-scale effects of neural interactions during human neocortical seizure activity.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets