Army develops big data approach to neuroscience (press release):
“A big data approach to neuroscience promises to significantly improve our understanding of the relationship between brain activity and performance.
To date, there have been relatively few attempts to use a big-data approach within the emerging field of neurotechnology. In this field, the few attempts at meta-analysis (analysis across multiple studies) combine only the results from individual studies rather than the raw data. A new study is one of the first to combine data across a diverse set of experiments to identify patterns of brain activity that are common across tasks and people.
The Army in particular is interested in how the cognitive state of Soldiers can affect their performance during a mission. If you can understand the brain, you can predict and even enhance cognitive performance.
Researchers from the U.S. Army Combat Capabilities Development Command’s Army Research Laboratory teamed with the University of Texas at San Antonio and Intheon Labs to develop a first-of-its-kind mega-analysis of brain imaging data–in this case electroencephalography, or EEG.
In the two-part paper, they aggregate the raw data from 17 individual studies, collected at six different locations, into a single analytical framework, with their findings published in a series of two papers in the journal NeuroImage. The individual studies included in this analysis encompass a diverse set of tasks such simulated driving and visual search.”
- From the abstract: Significant achievements have been made in the fMRI field by pooling statistical results from multiple studies (meta-analysis). More recently, fMRI standardization efforts have focused on enabling the joint analysis of raw fMRI data across studies (mega-analysis), with the hope of achieving more detailed insights. However, it has not been clear if such analyses in the EEG field are possible or equally fruitful. Here we present the results of a large-scale EEG mega-analysis using 18 studies from six sites representing several different experimental paradigms. We demonstrate that when meta-data are consistent across studies, both channel-level and source-level EEG mega-analysis are possible and can provide insights unavailable in single studies…In a companion paper, we apply mega-analysis to assess commonalities in event-related EEG features across studies. The continuous raw and preprocessed data used in this analysis are available through the DataCatalog at https://cancta.net.
- From the abstract: We present the results of a large-scale analysis of event-related responses based on raw EEG data from 17 studies performed at six experimental sites associated with four different institutions. The analysis corpus represents 1,155 recordings containing approximately 7.8 million event instances acquired under several different experimental paradigms. Such large-scale analysis is predicated on consistent data organization and event annotation as well as an effective automated preprocessing pipeline to transform raw EEG into a form suitable for comparative analysis…This work demonstrates that EEG mega-analysis (pooling of raw data across studies) can enable investigations of brain dynamics in a more generalized fashion than single studies afford. A companion paper complements this event-based analysis by addressing commonality of the time and frequency statistical properties of EEG across studies at the channel and dipole level.
News in Context:
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