Summary: Big data study combines information from a diverse set of experiments to identify patterns of brain activity common across people and tasks.
Source: U.S Army Research Laboratory
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.
“The vast majority of human neuroscientific studies use a very small number of participants employed in very specific tasks,” said Dr. Jonathan Touryan, an Army scientist and co-author of the paper. “This limits how well the results from any single study can be generalized to a broader population and a larger range of activities.”
Mega-analysis of EEG is extremely challenging due to the many types of hardware systems (properties and configuration of the electrodes), the diversity of tasks, how different datasets are annotated, and the intrinsic variability between individuals and within an individual over time, Touryan said.
These sources of variability make it difficult to find robust relationships between brain and behavior. Mega-analysis seeks to address this by aggregating large, heterogeneous datasets to identify universal features that link neural activity, cognitive state and task performance.
Next-generation neurotechnologies will require a thorough understanding of this relationship in order to mitigate deficits or augment performance of human operators. Ultimately, these neurotechnologies will enable autonomous systems to better understand the Soldier and facilitate communications within multi-domain operations, he said.
To combine the raw data from the collection of studies, the researchers developed Hierarchical Event Descriptors (HED tags) – a novel labeling ontology that captures the wide range of experimental events encountered in diverse datasets. This HED tag system was recently adopted into the Brain Imaging Data Structure international standard, one of the most common formats for organizing and analyzing brain data, Touryan said.
The research team also developed a fully automated processing pipeline to perform large-scale analysis of their high-dimensional time-series data–amounting to more than 1,000 recording sessions.
Much of this data was collected over the last 10 years through the U.S. Army’s Cognition and Neuroergonomics Collaborative Technology Alliance and is now available in an online repository for the scientific community. The U.S. Army continues to use this data to develop human-autonomy adaptive systems for both the Next Generation Combat Vehicle and Soldier Lethality Cross-Functional Teams.
About this neuroscience research article
Source: U.S Army Research Laboratory Media Contacts: Patti Riippa – U.S Army Research Laboratory Image Source: The image is credited to the U.S. Army.
Automated EEG mega-analysis I: Spectral and amplitude characteristics across studies
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. The analysis uses a fully-automated processing pipeline to reduce line noise, interpolate noisy channels, perform robust referencing, remove eye-activity, and further identify outlier signals. We define several robust measures based on channel amplitude and dispersion to assess the comparability of data across studies and observe the effect of various processing steps on these measures. Using ICA-based dipolar sources, we also observe consistent differences in overall frequency baseline amplitudes across brain areas. For example, we observe higher alpha in posterior vs anterior regions and higher beta in temporal regions. We also detect consistent differences in the slope of the aperiodic portion of the EEG spectrum across brain areas. 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.
Automated EEG mega-analysis II: Cognitive aspects of event related features
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. A key component of this analysis is the annotation of study-specific event codes using a common vocabulary to describe relevant event features. We demonstrate that Hierarchical Event Descriptors (HED tags) capture statistically significant cognitive aspects of EEG events common across multiple recordings, subjects, studies, paradigms, headset configurations, and experimental sites. We use representational similarity analysis (RSA) to show that EEG responses annotated with the same cognitive aspect are significantly more similar than those that do not share that cognitive aspect. These RSA similarity results are supported by visualizations that exploit the non-linear similarities of these associations. We apply temporal overlap regression, reducing confounds caused by adjacent event instances, to extract time and time-frequency EEG features (regressed ERPs and ERSPs) that are comparable across studies and replicate findings from prior, individual studies. Likewise, we use second-level linear regression to separate effects of different cognitive aspects on these features across all studies. 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.