Biology subjects

Zucchelli, M.

Publications and source records attributed to Zucchelli, M..

3 recordsLinked to original sources

bioRxiv2021

Subacute Inhalation of Ultrafine Particulate Matter Triggers Inflammation Without Altering Amyloid Beta Load in 5xFAD mice.

Epidemiological studies reveal that air pollution exposure may exacerbate neurodegeneration. Ultrafine particles (UFPs) are pollutants that remain unregulated in ambient air by environmental agencies. Due to their small size (<100nm), UFPs have the most potential to cross the bodily barriers and thus impact the brain. However, little information exists about how UFPs affect brain function. Alzheimers disease (AD) is the most common form of dementia, which has been linked to air pollutant exposure, yet limited information is available on the mechanistic connection between them. This study aims to decipher the effects of UFPs in the brain and periphery using the 5xFAD mouse model of AD. In our study design, AD mice and their wildtype littermates were subjected to 2-weeks inhalation exposure of UFPs in a whole-body chamber. That subacute exposure did not affect the blood-brain barrier integrity or amyloid-beta accumulation. However, when multiple cytokines were analyzed, we found increased levels of proinflammatory cytokines in the brain and periphery, with a predominant alteration of interferon-gamma in response to UFP exposure in both genotypes. Following exposure, mitochondrial superoxide dismutase was significantly upregulated only in the 5xFAD hippocampi, depicting oxidative stress induction in the exposed AD mouse group. These data demonstrate that short-term exposure to inhaled UFPs induces inflammation without affecting amyloid-beta load. This study provides a better understanding of adverse effects caused by short-term UFP exposure in the brain and periphery, also in the context of AD. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=138 SRC="FIGDIR/small/459017v1_ufig1.gif" ALT="Figure 1"> View larger version (27K): org.highwire.dtl.DTLVardef@479511org.highwire.dtl.DTLVardef@18bc59aorg.highwire.dtl.DTLVardef@15a0240org.highwire.dtl.DTLVardef@49f911_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

bioRxiv2021

On the generalizability of diffusion MRI signal representations across acquisition parameters, sequences and tissue types: chronicles of the MEMENTO challenge.

Diffusion MRI (dMRI) has become an invaluable tool to assess the microstructural organization of brain tissue. Depending on the specific acquisition settings, the dMRI signal encodes specific properties of the underlying diffusion process. In the last two decades, several signal representations have been proposed to fit the dMRI signal and decode such properties. Most methods, however, are tested and developed on a limited amount of data, and their applicability to other acquisition schemes remains unknown. With this work, we aimed to shed light on the generalizability of existing dMRI signal representations to different diffusion encoding parameters and brain tissue types. To this end, we organized a community challenge - named MEMENTO, making available the same datasets for fair comparisons across algorithms and techniques. We considered two state-of-the-art diffusion datasets, including single-diffusion-encoding (SDE) spin-echo data from a human brain with over 3820 unique diffusion weightings (the MASSIVE dataset), and double (oscillating) diffusion encoding data (DDE/DODE) of a mouse brain including over 2520 unique data points. A subset of the data sampled in 5 different voxels was openly distributed, and the challenge participants were asked to predict the remaining part of the data. After one year, eight participant teams submitted a total of 80 signal fits. For each submission, we evaluated the mean squared error, the variance of the prediction error and the Bayesian information criteria. Most predictions predicted either multi-shell SDE data (37%) or DODE data (22%), followed by cartesian SDE data (19%) and DDE (18%). Most submissions predicted the signals measured with SDE remarkably well, with the exception of low and very strong diffusion weightings. The prediction of DDE and DODE data seemed more challenging, likely because none of the submissions explicitly accounted for diffusion time and frequency. Next to the choice of the model, decisions on fit procedure and hyperparameters play a major role in the prediction performance, highlighting the importance of optimizing and reporting such choices. This work is a community effort to highlight strength and limitations of the field at representing dMRI acquired with trending encoding schemes, gaining insights into how different models generalize to different tissue types and fiber configurations over a large range of diffusion encodings.

neuroscience↗

bioRxiv2021

Multi-Tissue Multi-Compartment models of diffusion MRI

State-of-the-art multi-compartment microstructural models of diffusion MRI (dMRI) in the human brain have limited capability to model multiple tissues at the same time. In particular, the available techniques that allow this multi-tissue modelling are based on multi-TE acquisitions. In this work we propose a novel multi-tissue formulation of classical multi-compartment models that relies on more common single-TE acquisitions and can be employed in the analysis of previously acquired datasets. We show how modelling multiple tissues provides a new interpretation of the concepts of signal fraction and volume fraction in the context of multi-compartment modelling. The software that allows to inspect single-TE diffusion MRI data with multi-tissue multi-compartment models is included in the publicly available Dmipy Python package.

neuroscience↗