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BRIEF RESEARCH REPORT article
Front. Bioeng. Biotechnol.
Sec. Biosensors and Biomolecular Electronics
Volume 12 - 2024 |
doi: 10.3389/fbioe.2024.1390108
This article is part of the Research Topic Bioimaging Applications in Biosensors and Biomolecular Electronics View all 5 articles
DENOISING: Dynamic Enhancement and Noise Overcoming in Multimodal Neural Observations via High-density CMOS-based Biosensors
Provisionally accepted- 1 German Center for Neurodegenerative Diseases (DZNE), Dresden, Lower Saxony, Germany
- 2 TU Dresden, Faculty of Medicine Carl Gustav Carus, Dresden, Germany
Large-scale multimodal neural recordings on high-density biosensing microelectrode arrays (HD-MEAs) offer unprecedented insights into the dynamic interactions and connectivity across various brain networks. However, the fidelity of these recordings is frequently compromised by pervasive noise, which obscures meaningful neural information and complicates data analysis. To address this challenge, we introduce DENOISING, a versatile data-derived computational engine engineered to adjust thresholds adaptively based on large-scale extracellular signal characteristics and noise levels. This facilitates the separation of signal and noise components without reliance on specific data transformations. Uniquely capable of handling a diverse array of noise types (electrical, mechanical, and environmental) and multidimensional neural signals, including stationary and non-stationary oscillatory local field potential (LFP) and spiking activity, DENOISING presents an adaptable solution applicable across different recording modalities and brain networks. Applying DENOISING to largescale neural recordings from mice hippocampal and olfactory bulb networks yielded enhanced signalto-noise ratio (SNR) of LFP and spike firing patterns compared to those computed from raw data. Comparative analysis with existing state-of-the-art denoising methods, employing SNR and root mean square noise (RMS), underscores DENOISING's performance in improving data quality and reliability. Through experimental and computational approaches, we validate that DENOISING improves signal clarity and data interpretation by effectively mitigating independent noise in spatiotemporally structured multimodal datasets, thus unlocking new dimensions in understanding neural connectivity and functional dynamics.
Keywords: High-density microelectrode arrays, Large-scale neural recordings, Spike sorting and waveform clustering, Neural circuits/networks, neural dynamics
Received: 22 Feb 2024; Accepted: 27 Aug 2024.
Copyright: © 2024 Hu, Addison Emery, Khanzada and Amin. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Hayder Amin, German Center for Neurodegenerative Diseases (DZNE), Dresden, Lower Saxony, Germany
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