Shadow imaging for panoptical visualization of brain tissue in vivo

Dembitskaya, Yulia; Boyce, Andrew K. J.; Idziak, Agata; Pourkhalili, Atefeh; Bourdeelles, Guillaume Le; Girard, Jordan; Arizono, Misa; Ducros, Mathieu; Sato-Fitoussi, Marie; Oizel, Kristell; Bancelin, Stéphane; Mercier, Luc; Pfeiffer, Thomas; Thompson, Roger; Kim, Sun Kwang; Bikfalvi, Andréas; Nägerl, U. Valentin

doi:10.21203/rs.3.rs-2198041/v1

Cited by 3 publications

(3 citation statements)

References 21 publications

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“…In the midst of a wave of neuroimaging advances across scales [ 21 , 79 ], here high-fidelity inverse computational models create a bridge between multi-modal MR imaging data and biophysical clearance hypotheses; thus enabling a new technological avenue for identification and quantification of human brain solute transport mechanisms. Our findings highlight the combined roles and importance of extracellular diffusion, local clearance at rates comparable to tracer transport across the blood–brain barrier or advective velocities on the order of m/min sustained by local fluid influx or efflux, and reveal reduced advective flow after sleep-deprivation.…”

Section: Discussionmentioning

confidence: 99%

Human brain solute transport quantified by glymphatic MRI-informed biophysics during sleep and sleep deprivation

Vinje,

Zapf,

Ringstad

et al. 2023

Fluids Barriers CNS

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Whether you are reading, running or sleeping, your brain and its fluid environment continuously interacts to distribute nutrients and clear metabolic waste. Yet, the precise mechanisms for solute transport within the human brain have remained hard to quantify using imaging techniques alone. From multi-modal human brain MRI data sets in sleeping and sleep-deprived subjects, we identify and quantify CSF tracer transport parameters using forward and inverse subject-specific computational modelling. Our findings support the notion that extracellular diffusion alone is not sufficient as a brain-wide tracer transport mechanism. Instead, we show that human MRI observations align well with transport by either by an effective diffusion coefficent 3.5$$\times $$ × that of extracellular diffusion in combination with local clearance rates corresponding to a tracer half-life of up to 5 h, or by extracellular diffusion augmented by advection with brain-wide average flow speeds on the order of 1–9 $$\mu $$ μ m/min. Reduced advection fully explains reduced tracer clearance after sleep-deprivation, supporting the role of sleep and sleep deprivation on human brain clearance.

show abstract

Section: Discussionmentioning

confidence: 99%

Human brain solute transport quantified by glymphatic MRI-informed biophysics during sleep and sleep deprivation

Vinje,

Zapf,

Ringstad

et al. 2023

Fluids Barriers CNS

View full text Add to dashboard Cite

show abstract

“…In the midst of a wave of neuroimaging advances across scales 21, 70 , here high-fidelity inverse computational models create a bridge between multi-modal MR imaging data and biophysical clearance hypotheses; thus enabling a new technological avenue for identification and quantification of human brain solute transport mechanisms. Our findings highlight the combined roles and importance of extracellular diffusion, local clearance at rates comparable to tracer transport across the blood-brain barrier or advective velocities on the order of µ m/min sustained by local fluid influx or efflux, and reveal reduced advective flow after sleep-deprivation.…”

Section: Discussionmentioning

confidence: 99%

Human brain solute transport quantified by glymphatic MRI-informed biophysics during sleep and sleep deprivation

Vinje¹,

Zapf

Ringstad

et al. 2023

Preprint

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Whether you are reading, running or sleeping, your brain and its fluid environment continuously interacts to distribute nutrients and clear metabolic waste. Yet, the precise mechanisms for solute transport within the human brain have remained hard to quantify using imaging techniques alone. From multi-modal human brain MRI data sets in sleeping and sleep-deprived subjects, we identify and quantify CSF tracer transport parameters using forward and inverse subject-specific computational modelling. Our findings support the notion that extracellular diffusion alone is not sufficient as a brain-wide tracer transport mechanism. Instead, we show that human MRI observations align well with transport by either substantially enhanced (3.5 times faster) extracellular diffusion in combination with local clearance rates corresponding to a tracer half-life of up to 5 hours, or by extracellular diffusion augmented by advection with brain-wide average flow speeds on the order of 1-9 micrometers/min. Reduced advection fully explains reduced tracer clearance after sleep-deprivation, supporting the role of sleep and sleep deprivation on human brain clearance.

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“…While 2D annotated datasets are increasingly available [7,8], they are still scarce for 3D images due to the labor-intensive nature of accurate 3D image annotation. Moreover, because of the high cell density in 3D samples, even manually delineating individual cells is extremely challenging and requires specialized methods and sufficiently high resolution [9][10][11]. Therefore, DL-based 3D instance segmentation methods [12,13] are often limited to nuclei and tailored to specific datasets, and even here, annotation remains a major bottleneck.…”

mentioning

confidence: 99%

PAC-MAP: Proximity Adjusted Centroid Mapping for Accurate Detection of Nuclei in Dense 3D Cell Systems

Van De Looverbosch,

De Beuckeleer,

De Smet

et al. 2024

Preprint

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In the past decade, deep learning algorithms have surpassed the performance of many conventional image segmentation pipelines. Powerful models are now available for segmenting cells and nuclei in diverse 2D image types, but segmentation in 3D cell systems remains challenging due to the high cell density, the heterogenous resolution and contrast across the image volume, and the difficulty in generating reliable and sufficient ground truth data for model training. Reasoning that most image processing applications rely on nuclear segmentation but do not necessarily require an accurate delineation of their shapes, we implemented PAC-MAP, a 3D U-net based method that predicts the position of nuclei centroids and their proximity to other nuclei. We show that our model outperforms existing methods, predominantly by boosting recall, especially in conditions of high cell density. When trained from scratch PAC-MAP attained an average F1 score of 0.793 in dense spheroids. When pretraining using weakly supervised bulk data input and finetuning with few expert annotations the average F1 score could be significantly improved up to 0.817. We demonstrate the utility of our method for quantifying the cell content of spheroids and mapping the degree of glioblastoma multiforme infiltration in cerebral organoids.

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Shadow imaging for panoptical visualization of brain tissue in vivo

Cited by 3 publications

References 21 publications

Human brain solute transport quantified by glymphatic MRI-informed biophysics during sleep and sleep deprivation

Human brain solute transport quantified by glymphatic MRI-informed biophysics during sleep and sleep deprivation

Human brain solute transport quantified by glymphatic MRI-informed biophysics during sleep and sleep deprivation

PAC-MAP: Proximity Adjusted Centroid Mapping for Accurate Detection of Nuclei in Dense 3D Cell Systems

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