Biology subjects

Siviglia, A.

Publications and source records attributed to Siviglia, A..

3 recordsLinked to original sources

bioRxiv2025

MRS4Brain: a processing toolbox for preclinical MR spectroscopy and spectroscopic imaging data

ObjectivesMagnetic resonance spectroscopy is a non-invasive technique for probing metabolism and underpins advanced methods such as magnetic resonance spectroscopic imaging (MRSI) and diffusion-weighted spectroscopy (DWS). MRSI enables spatial mapping of metabolite distributions, offering insights into regional metabolic heterogeneity that single-voxel spectroscopy (SVS) cannot capture. However, MRSI produces large multidimensional datasets and requires complex processing pipelines, limiting reproducibility and accessibility. While human studies benefit from advanced processing tools, similar developments in preclinical research remain scarce, highlighting a demand for practical tools accessible to non-experts. MethodsTo address this need, we introduce the MRS4Brain Toolbox, a freely available MATLAB-based platform for preclinical spectroscopy, including MRSI, SVS, and DWS. ResultsThe toolbox integrates reconstruction, preprocessing, quantification, quality control, brain segmentation automatically overlaid on metabolite maps, modeling, and statistical analysis into unified workflows accessible via a graphical interface. ConclusionBy streamlining data processing and reducing technical barriers, MRS4Brain Toolbox promotes reproducibility, harmonization, and broader adoption of advanced spectroscopic techniques in preclinical studies, ultimately facilitating translational research.

neuroscience↗

bioRxiv2025

Metabolic modelling and time-resolved mapping of glucose oxidative metabolism in rats brain by indirect deuterium detection with 1H-FID-MRSI at 9.4T

ObjectThe present study exploits newly developed dynamic indirect 1H-[2H]-FID-MRSI at 9.4T, combined with a dedicated metabolic model, to enable regional and quantitative characterization of glucose oxidative metabolism flux in the rat brain with minimal metabolic assumptions, by measuring both 2H-labelled Glx turnover and pool size along a controlled 2H-Glc infusion protocol. Materials and MethodsSeven rats underwent dynamic 2D 1H-FID-MRSI during a 2-hour infusion of [6,6-2H2] glucose. Consecutive 13-min acquisitions quantified Glx-C4 1H-signal decay, converted to 2H-Glx concentrations using baseline metabolite pool sizes. A three-pool kinetic model including 2H-label loss was fitted to regional turnover curves to estimate oxidative flux (Vgt) and pyruvate dilution (Kdil). Model performance and parameter robustness were finally assessed with Monte-Carlo simulations. ResultsIn vivo 2H-Glx turnover showed a saturated exponential rise ([~]60 min), with a labelling plateau higher in striatum (1.85 mol/g) than hippocampus (1.55 mol/g). Metabolic modelling provided region-specific oxidative fluxes: Vgt = 0.27 {+/-} 0.07 mol/g/min (hippocampus) and Vgt = 0.40 {+/-} 0.06 mol/g/min (striatum), with consistent Kdil across regions. Simulations confirmed a good model robustness in retrieving Vgt over a large range of experimental conditions. DiscussionThis work shows the appropriateness of indirect dynamic 1H-[2H]-FID-MRSI for quantitative metabolic flux mapping of cerebral glucose oxidative metabolism.

neuroscience↗

bioRxiv2025

Towards harmonized spectral quantification in MRSI: Comparative analysis of Backward-Linear-Predicted and original 1H-FID-MRSI dephased data

ObjectWe hypothesized that the inherent acquisition delay (AD) in {superscript 1}H-FID-MRSI can introduce systematic LCModel quantification biases due to strong spectral dephasing, and that Backward-Linear-Prediction (BLP) reconstruction toward AD = 0 ms can harmonize metabolite estimates across acquisitions with various delays. Materials and Methods2D {superscript 1}H-FID-MRSI were acquired in rats at 14.1T with three AD values (0.71, 0.94, 1.30 ms). Hippocampal metabolites were quantified using LCModel and AD-matched basis sets. Complementary Monte-Carlo simulations (n = 1000) replicated {superscript 1}H-FID-MRSI spectra at multiple ADs under realistic SNR conditions. BLP was applied to in vivo and simulated FIDs to back-predict missing points up to AD = 0 ms, enabling quantification within a unified basis set framework. ResultsIn vivo and simulated data showed clear AD-dependent variations for several metabolites (Gln, tCho, tNAA, Ins, Tau), with discrepancies frequently >10% despite AD-specific basis sets. Simulations confirmed metabolite-specific biases increasing with AD. BLP reconstruction preserved quantification consistency up to [~]0.98 ms of recovered FIDs, reducing inter-AD mismatches in vivo--particularly for Tau, tNAA and tCho--lowering the mean discrepancy from 10.5% to [~]5%. DiscussionThese findings show that AD affects {superscript 1}H-FID-MRSI quantification in LCModel, whereas BLP reconstruction can harmonize spectra across delays by enabling a virtual AD = 0 ms quantification scheme. This supports BLP as a practical strategy to improve consistency and comparability in MRSI studies.

neuroscience↗