Purpose: Reliable detection and fitting of macromolecules (MM) are crucial for accurate quantification of brain short-echo time (TE) H-MR spectra. An experimentally acquired single MM spectrum is commonly used. Higher spectral resolution at ultra-high field (UHF) led to increased interest in using a parametrized MM spectrum together with flexible spline baselines to address unpredicted spectroscopic components. Herein, we aimed to: (1) implement an advanced methodological approach for post-processing, fitting, and parametrization of 9.4T rat brain MM spectra; (2) assess the concomitant impact of the LCModel baseline and MM model (ie, single vs parametrized); and (3) estimate the apparent T relaxation times for seven MM components.
Methods: A single inversion recovery sequence combined with advanced AMARES prior knowledge was used to eliminate the metabolite residuals, fit, and parametrize 10 MM components directly from 9.4T rat brain in vivo H-MR spectra at different TEs. Monte Carlo simulations were also used to assess the concomitant influence of parametrized MM and DKNTMN parameter in LCModel.
Results: A very stiff baseline (DKNTMN ≥ 1 ppm) in combination with a single MM spectrum led to deviations in metabolite concentrations. For some metabolites the parametrized MM showed deviations from the ground truth for all DKNTMN values. Adding prior knowledge on parametrized MM improved MM and metabolite quantification. The apparent T ranged between 12 and 24 ms for seven MM peaks.
Conclusion: Moderate flexibility in the spline baseline was required for reliable quantification of real/experimental spectra based on in vivo and Monte Carlo data. Prior knowledge on parametrized MM improved MM and metabolite quantification.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8596437 | PMC |
http://dx.doi.org/10.1002/mrm.28910 | DOI Listing |
Proc Natl Acad Sci U S A
January 2025
Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405.
Dysregulation of GABAergic inhibition is associated with pathological pain. Consequently, enhancement of GABAergic transmission represents a potential analgesic strategy. However, therapeutic potential of current GABA agonists and modulators is limited by unwanted side effects.
View Article and Find Full Text PDFProc Natl Acad Sci U S A
January 2025
Department of Neurophysiology, Medical Faculty, Ruhr University Bochum, Bochum 44780, Germany.
The novelty, saliency, and valency of ongoing experiences potently influence the firing rate of the ventral tegmental area (VTA) and the locus coeruleus (LC). Associative experience, in turn, is recorded into memory by means of hippocampal synaptic plasticity that is regulated by noradrenaline sourced from the LC, and dopamine, sourced from both the VTA and LC. Two persistent forms of synaptic plasticity, long-term potentiation (LTP), and long-term depression (LTD) support the encoding of different kinds of spatial experience.
View Article and Find Full Text PDFAdv Sci (Weinh)
January 2025
Key Laboratory of Mental Disorders, The Second Hospital of Shandong University, School of Basic Medical Sciences, Shandong University, Jinan, Shandong, 250012, China.
Major depressive disorder (MDD) is usually considered associate with immune inflammation and synaptic injury within specific brain regions. However, the molecular mechanisms underlying the neural deterioration resulting in depression remain unclear. Here, it is found that miR-204-5p is markedly downregulated in the ventromedial prefrontal cortex (vmPFC) in a chronic unpredictable mild stress (CUMS) induce rat model of depression.
View Article and Find Full Text PDFFEBS J
January 2025
Department of Pharmacology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Germany.
Creatine is essential for ATP regeneration in energy-demanding cells. Creatine deficiency results in severe neurodevelopmental impairments. In the brain, creatine is synthesized locally by oligodendrocytes to supply neighboring neurons.
View Article and Find Full Text PDFElife
January 2025
Department of Neuroscience, Physiology and Pharmacology, University College London, London, United Kingdom.
Data-driven models of neurons and circuits are important for understanding how the properties of membrane conductances, synapses, dendrites, and the anatomical connectivity between neurons generate the complex dynamical behaviors of brain circuits in health and disease. However, the inherent complexity of these biological processes makes the construction and reuse of biologically detailed models challenging. A wide range of tools have been developed to aid their construction and simulation, but differences in design and internal representation act as technical barriers to those who wish to use data-driven models in their research workflows.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!