| Literature DB >> 29209195 |
James F Cavanagh1, Arthur Napolitano2, Christopher Wu2, Abdullah Mueen2.
Abstract
Electroencephalographic (EEG) recordings are thought to reflect the network-wide operations of canonical neural computations, making them a uniquely insightful measure of brain function. As evidence of these virtues, numerous candidate biomarkers of different psychiatric and neurological diseases have been advanced. Presumably, we would only need to apply powerful machine-learning methods to validate these ideas and provide novel clinical tools. Yet, the reality of this advancement is more complex: the scale of data required for robust and reliable identification of a clinical biomarker transcends the ability of any single laboratory. To surmount this logistical hurdle, collective action and transparent methods are required. Here we introduce the Patient Repository of EEG Data + Computational Tools (PRED+CT: predictsite.com). The ultimate goal of this project is to host a multitude of available tasks, patient datasets, and analytic tools, facilitating large-scale data mining. We hope that successful completion of this aim will lead to the development of novel EEG biomarkers for differentiating populations of neurological and psychiatric disorders.Entities:
Keywords: EEG; clinical neuroscience; databases as topic; open data; pattern classification
Year: 2017 PMID: 29209195 PMCID: PMC5702317 DOI: 10.3389/fninf.2017.00067
Source DB: PubMed Journal: Front Neuroinform ISSN: 1662-5196 Impact factor: 4.081