Statistically unbiased prediction enables accurate denoising of voltage imaging data

Eom, Minho; Han, Seungjae; Kim, Gyuri; Cho, Eun-Seo; Sim, Jueun; Park, Pojeong; Lee, Kang-Han; Kim, Seonghoon; Rózsa, Márton; Svoboda, Karel; Choi, Myunghwan; Kim, Cheol-Hee; Cohen, Adam E.; Chang, Jae-Byum; Yoon, Young-Gyu

doi:10.1101/2022.11.17.516709

Cited by 8 publications

(16 citation statements)

References 51 publications

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“…Furthermore, our study aligns with the growing body of research emphasizing the critical need for high-fidelity neural data to understand complex brain functions and disorders (10,11,35,36), necessitating multi-purpose denoising methods (32,37,38).…”

Section: Discussionmentioning

confidence: 53%

“…While the DENOISING framework has shown substantial efficacy in enhancing the clarity of extracellular recordings through dynamic adjustment and noise separation, it does not incorporate the application of deep learning approaches exemplified by other methods (32,37,38). However, by not utilizing a machine learning backbone or extensive training datasets, DENOISING offers a significant computational advantage compared to other methods.…”

Section: Discussionmentioning

confidence: 99%

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DENOISING: Dynamic Enhancement and Noise Overcoming in Multimodal Neural Observations via High-density CMOS-based Biosensors

Hu,

Emery,

Khanzada

et al. 2024

Preprint

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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.

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Section: Discussionmentioning

confidence: 53%

Section: Discussionmentioning

confidence: 99%

DENOISING: Dynamic Enhancement and Noise Overcoming in Multimodal Neural Observations via High-density CMOS-based Biosensors

Hu,

Emery,

Khanzada

et al. 2024

Preprint

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“…Within the 30 imaging z-planes of the brain, we identified 25556 neuron ROIs, accounting for ∼33% of the total ∼78000 neurons estimated in the larval zebrafish brain 52 . It is worth noting this percentage may be a lower limit estimate on the proportion of neurons in the brain that can be extracted using our microscope, as our annotation was performed visually on single z-plane images and raw time series videos, and many more neurons could be annotated in the future with the help of image denoising 53 and signal unmixing 54 algorithms. We were deliberately conservative in this paper’s analysis, so that we could focus on the question of whether our microscope design crossed the threshold of being able to image neural activity distributed throughout a zebrafish brain.…”

Section: Resultsmentioning

confidence: 99%

Imaging the voltage of neurons distributed across entire brains of larval zebrafish

Wang,

Zhang,

Symvoulidis

et al. 2023

Preprint

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Neurons interact in networks distributed throughout the brain. Although much effort has focused on whole-brain calcium imaging, recent advances in genetically encoded voltage indicators (GEVIs) raise the possibility of imaging voltage of neurons distributed across brains. To achieve this, a microscope must image at high volumetric rate and signal-to-noise ratio. We present a remote scanning light-sheet microscope capable of imaging GEVI-expressing neurons distributed throughout entire brains of larval zebrafish at a volumetric rate of 200.8 Hz. We measured voltage of ∼1/3 of the neurons of the brain, distributed throughout. We observed that neurons firing at different times during a sequence were located at different brain locations, for sequences elicited by a visual stimulus, which mapped onto locations throughout the optic tectum, as well as during stimulus-independent bursts, which mapped onto locations in the cerebellum and medulla. Whole-brain voltage imaging may open up frontiers in the fundamental operation of neural systems.

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“…Representative images were denoised using SUPPORT denoising software. 31 Deep Learning-based NT tip tracking NT motility was assessed using an automated pipeline that utilises deep learning for NT tip detection and a linear assignment problem (LAP) tracker to link detected tips. We trained a fully convolutional neural network using nnU-Net.…”

Section: Confocal Fluorescence and Reflection Microscopy Imagingmentioning

confidence: 99%

Structure and dynamics of human cardiac fibroblast nanotubes

Schmid-Herbstritt,

Stief,

Greiner

et al. 2023

Preprint

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Efficient and dynamic interactions between cardiac fibroblasts and their environment are essential for the maintenance of tissue homeostasis in healthy hearts and play an important role during pathological remodelling. Here, we investigate a relatively obscure mechanism through which human atrial fibroblasts communicate with each other, with other cells, and with the extracellular matrix (ECM) – nanotubes (NT). We investigated NT structure and dynamics in primary right atrial fibroblasts isolated from patients in sinus rhythm (SR) and atrial fibrillation (AF), in an immortalised human atrial fibroblasts cell line, and in intact human tissue, using a wide range of imaging approaches (including confocal microscopy, label-free reflection microscopy, rotating coherent scattering microscopy, and cryo-electron tomography). We show that fibroblasts maintain continuous NT activityin vitro, with numerous protrusions constantly probing the surrounding environment. NT structure and activity change during AF and following pharmacological (transforming growth factor-β, latrunculin B) and environmental (hypoxia) interventions. We also show that cardiac fibroblast NT mediate intercellular organelle exchange and dynamically interact with ECM. Finally, we present evidence for the presence of fibroblast-borne NT in human atrial tissue. Our results advance our understanding of how cardiac fibroblasts interact with their environment. NT are versatile structures capable of both sensory and actuating functions, and offer a dynamic and rapid communication conduit that facilitates cell–cell and cell–extracellular matrix interactions.

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Statistically unbiased prediction enables accurate denoising of voltage imaging data

Cited by 8 publications

References 51 publications

DENOISING: Dynamic Enhancement and Noise Overcoming in Multimodal Neural Observations via High-density CMOS-based Biosensors

DENOISING: Dynamic Enhancement and Noise Overcoming in Multimodal Neural Observations via High-density CMOS-based Biosensors

Imaging the voltage of neurons distributed across entire brains of larval zebrafish

Structure and dynamics of human cardiac fibroblast nanotubes

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