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Electrical signatures characteristic of complex neurological activity and neuropsychiatric disease are embedded in electroencephalography (EEG) signal data. To firmly establish new correlations between these brain electrical pulses and cognition, behavior, and disorders, researchers must achieve adequate statistical power to validate and mitigate uncertainties in their findings. This necessitates the usage of extensive studies involving large volumes of raw EEG data files from multiple subjects, data which must be preprocessed before conducting further analysis. While conventional processing and analysis of these raw data have been performed using isolated physical lab environments and stovepiped applications, there is a growing necessity for processing and analysis solutions that enable distributed processing of large data collections. This study presents a novel microservices approach as an alternative and complementary solution for retrieving and preprocessing EEG signal data. The approach leverages serverless technologies to deliver a highly scalable solution for processing massive amounts of EEG data. Deployed within a public cloud environment, we assess the efficacy of this method when employing various container orchestration configurations. This work demonstrates the capability for substantial enhancements in processing speeds, particularly when dealing with extensive EEG datasets.
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Volitional Regulation and Transferable Patterns of Midbrain Oscillations
Dopaminergic brain areas are crucial for cognition and their dysregulation is linked to neuropsychiatric disorders typically treated with pharmacological interventions. These treatments often have side effects and variable effectiveness, underscoring the need for alternatives. We introduce the first demonstration of neurofeedback using local field potentials (LFP) from the ventral tegmental area (VTA). This approach leverages the real-time temporal resolution of LFP and ability to target deep brain. In our study, two male rhesus macaque monkeys (Macaca mulatta) learned to regulate VTA beta power using a customized normalized metric to stably quantify VTA LFP signal modulation. The subjects demonstrated flexible and specific control with different strategies for specific frequency bands, revealing new insights into the plasticity of VTA neurons contributing to oscillatory activity that is functionally relevant to many aspects of cognition. Excitingly, the subjects showed transferable patterns, a key criterion for clinical applications beyond training settings. This work provides a foundation for neurofeedback-based treatments, which may be a promising alternative to conventional approaches and open new avenues for understanding and managing neuropsychiatric disorders.
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- Award ID(s):
- 2145412
- PAR ID:
- 10570408
- Publisher / Repository:
- DOI PREFIX: 10.1523
- Date Published:
- Journal Name:
- The Journal of Neuroscience
- Volume:
- 45
- Issue:
- 13
- ISSN:
- 0270-6474
- Format(s):
- Medium: X Size: Article No. e1808242025
- Size(s):
- Article No. e1808242025
- Sponsoring Org:
- National Science Foundation
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