Dataset Information

Development of the error-monitoring system from ages 9-35: Unique insight provided by MRI-constrained source localization of EEG.

ABSTRACT: The ability to self-detect errors and dynamically adapt behavior is a cornerstone of higher-level cognition, requiring coordinated activity from a network of neural regions. However, disagreement exists over how the error-monitoring system develops throughout adolescence and early adulthood. The present report leveraged MRI-constrained EEG source localization to detail typical development of the error-monitoring system in a sample of 9-35 year-olds (n = 43). Participants performed a flanker task while high-density EEG was recorded; structural MRIs were also acquired for all participants. Analysis of the scalp-recorded EEG data revealed a frontocentral negativity (error-related negativity; ERN) immediately following errors for all participants, although the topography of the ERN varied with age. Source localization of the ERN time range revealed maximal activity within the posterior cingulate cortex (PCC) for all ages, consistent with recent evidence that the PCC provides a substantial contribution to the scalp-recorded ERN. Activity within a network of brain regions, including dorsal anterior cingulate, PCC, and parietal cortex, was predictive of improved performance following errors, regardless of age. However, additional activity within insula, orbitofrontal cortex and inferior frontal gyrus linearly increased with age. Together, these data suggest that the core error-monitoring system is online by early adolescence and remains relatively stable into adulthood. However, additional brain regions become embedded within this core network with age. These results serve as a model of typical development of the error-monitoring system from early adolescence into adulthood.

SUBMITTER: Buzzell GA

PROVIDER: S-EPMC5600828 | biostudies-literature | 2017 Aug

REPOSITORIES: biostudies-literature

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Development of the error-monitoring system from ages 9-35: Unique insight provided by MRI-constrained source localization of EEG.

Buzzell George A GA Richards John E JE White Lauren K LK Barker Tyson V TV Pine Daniel S DS Fox Nathan A NA

NeuroImage 20170524

The ability to self-detect errors and dynamically adapt behavior is a cornerstone of higher-level cognition, requiring coordinated activity from a network of neural regions. However, disagreement exists over how the error-monitoring system develops throughout adolescence and early adulthood. The present report leveraged MRI-constrained EEG source localization to detail typical development of the error-monitoring system in a sample of 9-35 year-olds (n = 43). Participants performed a flanker task ...[more]

PMID: 28549796

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Project description: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.

Dataset Information

Development of the error-monitoring system from ages 9-35: Unique insight provided by MRI-constrained source localization of EEG.

Publications

Development of the error-monitoring system from ages 9-35: Unique insight provided by MRI-constrained source localization of EEG.

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