Characterizing extracellular diffusion properties using diffusion-weighted MRS of sucrose injected in mouse brain

Cluster Analysis of VERDICT MRI for Cancer Tissue Characterization in Neuroendocrine Tumors

Diffusion MRI models accounting for varying diffusion times and high b-values, such as VERDICT, hold potential for non-invasively characterizing tumor tissue types, potentially enabling improved tumor grading, and treatment evaluation. Furthermore, cluster analysis can aid in identifying multidimensional patterns in the diffusion MRI (dMRI) data that are not apparent when analyzing individual parameters in isolation. The aim of this study was to evaluate how well cluster analysis of VERDICT parameters can be used for intratumor tissue characterization compared to ADC in a mouse model of human small intestine neuroendocrine tumor (GOT1), and to validate the method by histological analysis. Mice implanted with GOT1 were irradiated and subsequently imaged using a dMRI protocol designed for estimation of VERDICT parameters and ADC values. Histological analysis using hematoxylin and eosin (H&E), Masson's trichrome, and Ki67 staining identified three distinct tumor tissue types: necrotic, fibrotic, and viable tumor tissue. ROIs were drawn on regions of high and low ADC, which spatially matched with necrosis or fibrosis, and viable tumor tissue, respectively. Among the VERDICT parameters, the cell radius index (R) was most effective in distinguishing between necrotic and fibrotic tissue, whereas the intracellular fraction (fIC) was the most effective in differentiating viable from non-viable tissue. A Gaussian mixture model (GMM) of three clusters, representing each tumor tissue type, was fitted to R and fIC of all tumor voxel data. VERDICT cluster maps corresponded well with the histology classification maps overall. Fibrotic tissue corresponded best with the cluster of low fIC and low R, necrotic tissue with the cluster of low fIC and high R, and viable tumor tissue with the cluster of high fIC and intermediate R. In conclusion, GMM cluster analysis of VERDICT MRI data shows potential in differentiating necrotic, fibrotic, and viable tumor tissue in irradiated GOT1 tumors.

Fluid flow and amyloid transport and aggregation in the brain interstitial space

The driving mechanisms at the base of the clearance of biological wastes in the brain interstitial space (ISS) are still poorly understood and an actively debated subject. A complete comprehension of the processes that lead to the aggregation of amyloid proteins in such environment, hallmark of the onset and progression of Alzheimer's disease, is of crucial relevance. Here we employ combined computational fluid dynamics and molecular dynamics techniques to uncover the role of fluid flow and proteins transport in the brain ISS. Our work identifies diffusion as the principal mechanism for amyloid-β proteins clearance, whereas fluid advection may lead transport for larger molecular bodies, like amyloid-β aggregates or extracellular vesicles. We also clearly quantify the impact of large nascent prefibrils on the fluid flowing and shearing. Finally, we show that, even in the irregular brain interstitial space (ISS), hydrodynamic interactions enhance amyloid-β aggregation at all stages of the aggregation pathway. Our results are key to understand the role of fluid flow and solvent-solute interplay on therapeutics like antibodies acting in the brain ISS.

Capturing alterations of intracellular-extracellular lactate distribution in the brain using diffusion-weighted MR spectroscopy in vivo

While the intracellular-extracellular distribution of lactate has been suggested to play a critical role in the healthy and diseased brain, tools are lacking to noninvasively probe lactate in intracellular and extracellular spaces. Here, we show that, by measuring the diffusion of lactate with diffusion-weighted magnetic resonance (MR) spectroscopy in vivo and comparing it to the diffusion of purely intracellular metabolites, noninvasive quantification of extracellular and intracellular lactate fractions becomes possible. More specifically, we detect alterations of lactate diffusion in the APP/PS1 mouse model of Alzheimer's disease. Data modeling allows quantifying decreased extracellular lactate fraction in APP/PS1 mice as compared to controls, which is quantitatively confirmed with implanted enzyme-microelectrodes. The capability of diffusion-weighted MR spectroscopy to quantify extracellular-intracellular lactate fractions opens a window into brain metabolism, including in Alzheimer's disease.

A Simulation of the Effects of Diffusion on Hyperpolarized [1-13C]-Pyruvate Signal Evolution

OBJECTIVE

Hyperpolarized [1-13C]-pyruvate magnetic resonance imaging is an emerging metabolic imaging method that offers unprecedented spatiotemporal resolution for monitoring tumor metabolism in vivo. To establish robust imaging biomarkers of metabolism, we must characterize phenomena that may modulate the apparent pyruvate-to-lactate conversion rate (kPL). Here, we investigate the potential effect of diffusion on pyruvate-to-lactate conversion, as failure to account for diffusion in pharmacokinetic analysis may obscure true intracellular chemical conversion rates.

METHODS

Changes in hyperpolarized pyruvate and lactate signal were calculated using a finite-difference time domain simulation of a two-dimensional tissue model. Signal evolution curves with intracellular kPL values from 0.02 to 1.00 s-1 were analyzed using spatially invariant one-compartment and two-compartment pharmacokinetic models. A second spatially variant simulation incorporating compartmental instantaneous mixing was fit with the same one-compartment model.

RESULTS

When fitting with the one-compartment model, apparent kPL underestimated intracellular kPL by approximately 50% at an intracellular kPL of 0.02 s-1. This underestimation increased for larger kPL values. However, fitting the instantaneous mixing curves showed that diffusion accounted for only a small part of this underestimation. Fitting with the two-compartment model yielded more accurate intracellular kPL values.

SIGNIFICANCE

This work suggests diffusion is not a significant rate-limiting factor in pyruvate-to-lactate conversion given that our model assumptions hold true. In higher order models, diffusion effects may be accounted for by a term characterizing metabolite transport. Pharmacokinetic models used to analyze hyperpolarized pyruvate signal evolution should focus on carefully selecting the analytical model for fitting rather than accounting for diffusion effects.

Event-related functional magnetic resonance spectroscopy

Proton-Magnetic Resonance Spectroscopy (MRS) is a non-invasive brain imaging technique used to measure the concentration of different neurochemicals. "Single-voxel" MRS data is typically acquired across several minutes, before individual transients are averaged through time to give a measurement of neurochemical concentrations. However, this approach is not sensitive to more rapid temporal dynamics of neurochemicals, including those that reflect functional changes in neural computation relevant to perception, cognition, motor control and ultimately behaviour. In this review we discuss recent advances in functional MRS (fMRS) that now allow us to obtain event-related measures of neurochemicals. Event-related fMRS involves presenting different experimental conditions as a series of trials that are intermixed. Critically, this approach allows spectra to be acquired at a time resolution in the order of seconds. Here we provide a comprehensive user guide for event-related task designs, choice of MRS sequence, analysis pipelines, and appropriate interpretation of event-related fMRS data. We raise various technical considerations by examining protocols used to quantify dynamic changes in GABA, the primary inhibitory neurotransmitter in the brain. Overall, we propose that although more data is needed, event-related fMRS can be used to measure dynamic changes in neurochemicals at a temporal resolution relevant to computations that support human cognition and behaviour.

A neuro-metabolic account of why daylong cognitive work alters the control of economic decisions

Behavioral activities that require control over automatic routines typically feel effortful and result in cognitive fatigue. Beyond subjective report, cognitive fatigue has been conceived as an inflated cost of cognitive control, objectified by more impulsive decisions. However, the origins of such control cost inflation with cognitive work are heavily debated. Here, we suggest a neuro-metabolic account: the cost would relate to the necessity of recycling potentially toxic substances accumulated during cognitive control exertion. We validated this account using magnetic resonance spectroscopy (MRS) to monitor brain metabolites throughout an approximate workday, during which two groups of participants performed either high-demand or low-demand cognitive control tasks, interleaved with economic decisions. Choice-related fatigue markers were only present in the high-demand group, with a reduction of pupil dilation during decision-making and a preference shift toward short-delay and little-effort options (a low-cost bias captured using computational modeling). At the end of the day, high-demand cognitive work resulted in higher glutamate concentration and glutamate/glutamine diffusion in a cognitive control brain region (lateral prefrontal cortex [lPFC]), relative to low-demand cognitive work and to a reference brain region (primary visual cortex [V1]). Taken together with previous fMRI data, these results support a neuro-metabolic model in which glutamate accumulation triggers a regulation mechanism that makes lPFC activation more costly, explaining why cognitive control is harder to mobilize after a strenuous workday.

Assessing potential correlation between T2 relaxation and diffusion of lactate in the mouse brain

Purpose

While diffusion and T₂ relaxation are intertwined, little or no correlation exists between diffusion and T₂ relaxation of intracellular metabolites in the rodent brain, as measured by diffusion‐weighted MRS at different TEs. However, situation might be different for lactate, since it is present in both extracellular and intracellular spaces, which exhibit different diffusion properties and may also exhibit different T₂. Such a TE dependence would be crucial to account for when interpreting or modeling lactate diffusion. Here we propose to take advantage of a new diffusion sequence, where J‐modulation of lactate is canceled even at long TE, thus retaining excellent signal, to assess potential T₂ dependence on diffusion of lactate in the mouse brain.

Methods

Using a frequency‐selective diffusion‐weighted spin‐echo sequence that removes J‐modulation at 1.3 ppm, thus preserving lactate signal even at long TE, we investigate the effect of TE between 50.9 and 110.9 ms (while keeping diffusion time constant) on apparent diffusivity and kurtosis in the mouse brain.

Results

Regardless of the metabolites, no difference appears for the diffusion‐weighted signal attenuation with increasing TE. For lactate, apparent diffusivity and kurtosis remain unchanged as TE increases.

Conclusion

No significant TE dependence of diffusivity and kurtosis is measured for lactate in the 50–110 ms TE range, confirming that potential T₂ effects can be ignored when interpreting or modeling lactate diffusion.

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