Journal Article

DZNE-2024-01161

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png DENOISING: Dynamic enhancement and noise overcoming in multimodal neural observations via high-density CMOS-based biosensors.

Hu, X. (First author)DZNE* ; Emery, B. A.DZNE* ; Khanzada, S.DZNE* ; Amin, H. (Last author)DZNE*

2024
Frontiers Media Lausanne

This record in other databases:    

Please use a persistent id in citations: doi:10.3389/fbioe.2024.1390108

Abstract: 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 large-scale neural recordings from mice hippocampal and olfactory bulb networks yielded enhanced signal-to-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.

Keyword(s): high-density microelectrode arrays ; large-scale neural recordings ; neural circuits/networks ; neural dynamics ; spike sorting ; waveform clustering

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Contributing Institute(s):

1. Biohybrid Neuroelectronics (BIONICS) (AG Amin)

Research Program(s):

1. 351 - Brain Function (POF4-351) (POF4-351)

Appears in the scientific report 2024

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Database coverage:
; ; ; ; Article Processing Charges ; BIOSIS Previews ; Biological Abstracts ; Clarivate Analytics Master Journal List ; Current Contents - Life Sciences ; DOAJ Seal ; Essential Science Indicators ; Fees ; IF >= 5 ; JCR ; PubMed Central ; SCOPUS ; Science Citation Index Expanded ; Web of Science Core Collection

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Linked articles:

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Dataset Amin, H.DZNE* ; Hu, X. (Project Member)DZNE* ; Emery, B. A. (Project Member)DZNE* ; Khanzada, S. (Project Member)DZNE*
Dataset: Large-scale Neural Recordings for DENOISING Engine, v1
Zenodo (2024) [10.5281/ZENODO.13284452]2024 BibTeX | EndNote: XML, Text | RIS

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