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  Signal detection in extracellular neural ensemble recordings using higher criticism

Fathizadeh, F., Mitricheva, E., Kimura, R., Logothetis, N., & Noori, H. (2019). Signal detection in extracellular neural ensemble recordings using higher criticism. In IEEE-EMBS International Conference on Biomedical and Health Informatics (IEEE-EMBS BHI 2019) (pp. 1-5).

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Item Permalink: http://hdl.handle.net/21.11116/0000-0003-C431-8 Version Permalink: http://hdl.handle.net/21.11116/0000-0003-C437-2
Genre: Conference Paper

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https://arxiv.org/pdf/1905.06225.pdf (Any fulltext)
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 Creators:
Fathizadeh, F1, 2, Author              
Mitricheva, E2, 3, Author              
Kimura, R1, 2, Author              
Logothetis, NK1, 2, Author              
Noori, HR2, 3, Author              
Affiliations:
1Department Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497798              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497794              
3Research Group Neuronal Convergence, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_2528694              

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 Abstract: Information processing in the brain is conducted by a concerted action of multiple neural populations. Gaining insights in the organization and dynamics of such populations can best be studied with broadband intracranial recordings of so-called extracellular field potential, reflecting neuronal spiking as well as mesoscopic activities, such as waves, oscillations, intrinsic large deflections, and multiunit spiking activity. Such signals are critical for our understanding of how neuronal ensembles encode sensory information and how such information is integrated in the large networks underlying cognition. The aforementioned principles are now well accepted, yet the efficacy of extracting information out of the complex neural data, and their employment for improving our understanding of neural networks, critically depends on the mathematical processing steps ranging from simple detection of action potentials in noisy traces - to fitting advanced mathematical models to distinct patterns of the neural signal potentially underlying intra-processing of information, e.g. interneuronal interactions. Here, we present a robust strategy for detecting signals in broadband and noisy time series such as spikes, sharp waves and multi-unit activity data that is solely based on the intrinsic statistical distribution of the recorded data. By using so-called higher criticism - a second-level significance testing procedure comparing the fraction of observed significances to an expected fraction under the global null - we are able to detect small signals in correlated noisy time-series without prior filtering, denoising or data regression. Results demonstrate the efficiency and reliability of the method and versatility over a wide range of experimental conditions and suggest the appropriateness of higher criticism to characterize neuronal dynamics without prior manipulation of the data.

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 Dates: 2019-05
 Publication Status: Published online
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Title: IEEE-EMBS International Conference on Biomedical and Health Informatics (IEEE-EMBS BHI 2019)
Place of Event: Chicago, IL, USA
Start-/End Date: 2019-05-19 - 2019-05-22

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Title: IEEE-EMBS International Conference on Biomedical and Health Informatics (IEEE-EMBS BHI 2019)
Source Genre: Proceedings
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Pages: - Volume / Issue: - Sequence Number: BHI-T-103 Start / End Page: 1 - 5 Identifier: -