SLIPMAT: A pipeline for extracting tissue-specific spectral profiles from 1H MR spectroscopic imaging data

Vella, Olivia; Bagshaw, Andrew P.; Wilson, Martin

doi:10.1016/j.neuroimage.2023.120235

Cited by 2 publications

(2 citation statements)

References 37 publications

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“…In contrast to conventional spectral fitting, no assumptions about the basis-set are made, avoiding one potential source of variability between analyses (Demler et al, 2024). Simple linear modelling is performed directly on spectral data points using the "mass univariate" approach, which is well-established for fMRI, and is becoming a more popular tool for electrophysiology (Quinn et al, 2024) and conventional MRS (Vella et al, 2023;Wu et al, 2022). While further validation of the approach is required, we show here how it may be used to support or question findings from conventional analyses.…”

Section: Discussionmentioning

confidence: 99%

“…The standard approach for fMRS analysis involves temporal averaging of spectra to boost SNR, and therefore the accuracy of metabolite estimates, followed by the use of conventional spectral fitting algorithms to measure dynamic metabolite changes. More recently, we have shown how the application of multiple univariate statistical tests directly to individual MRS data points can support findings from spectral fitting, and potentially reveal novel information on individual differences in neurometabolic profiles (Vella et al, 2023;Wu et al, 2022). A similar approach may be easily adapted to fMRS, where each frequency domain data point can be treated as an independent time course to be fitted with a linear model incorporating the predicted metabolite dynamics.…”

Section: Exploratory Fmrs Spectro-temporal Statistical Modellingmentioning

confidence: 94%

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Functional Magnetic Resonance Spectroscopy of Prolonged Motor Activation using Conventional and Spectral GLM Analyses

Morelli,

Dudzikowska,

Deelchand

et al. 2024

Preprint

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Background: Functional MRS (fMRS) is a technique used to measure metabolic changes in response to increased neuronal activity, providing unique insights into neurotransmitter dynamics and neuroenergetics. In this study we investigate the response of lactate and glutamate levels in the motor cortex during a sustained motor task using conventional spectral fitting and explore the use of a novel analysis approach based on the application of linear modelling directly to the spectro-temporal fMRS data. Methods: fMRS data were acquired at a field strength of 3 Tesla from 23 healthy participants using a short echo-time (28ms) semi-LASER sequence. The functional task involved rhythmic hand clenching over a duration of 8 minutes and standard MRS preprocessing steps, including frequency and phase alignment, were employed. Both conventional spectral fitting and direct linear modelling were applied, and results from participant-averaged spectra and metabolite-averaged individual analyses were compared. Results: We observed a 20% increase in lactate in response to the motor task, consistent with findings at higher magnetic field strengths. However, statistical testing showed some variability between the two averaging schemes and fitting algorithms. While lactate changes were supported by the direct spectral modelling approach, smaller increases in glutamate (2%) were inconsistent. Exploratory spectral modelling identified a 4% decrease in aspartate, aligning with conventional fitting and observations from prolonged visual stimulation. Conclusion: We demonstrate that lactate dynamics in response to a prolonged motor task are observed using short-echo time semi-LASER at 3 Tesla, and that direct linear modelling of fMRS data is a useful complement to conventional analysis. Future work includes mitigating spectral confounds, such as scalp lipid contamination and lineshape drift, and further validation of our novel direct linear modelling approach through experimental and simulated datasets.

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Section: Discussionmentioning

confidence: 99%

Section: Exploratory Fmrs Spectro-temporal Statistical Modellingmentioning

confidence: 94%

Functional Magnetic Resonance Spectroscopy of Prolonged Motor Activation using Conventional and Spectral GLM Analyses

Morelli,

Dudzikowska,

Deelchand

et al. 2024

Preprint

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NMR in living systems

Prior

2024

Nuclear Magnetic Resonance

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This chapter reviews studies published during the period of May 2023 to April 2024 that have applied magnetic resonance spectroscopy to investigate processes occurring in living systems. In Section 1, new hardware, acquisition methods and analytical processes that are applicable to in vivo investigations are presented. Studies in pre-clinical models and the clinical environment are surveyed in Sections 2 and 3, respectively. The review in both these two sections is subdivided into physiological categories, with each of these sub-divided according to the category of disease or the type of metabolic investigation.

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SLIPMAT: A pipeline for extracting tissue-specific spectral profiles from 1H MR spectroscopic imaging data

Cited by 2 publications

References 37 publications

Functional Magnetic Resonance Spectroscopy of Prolonged Motor Activation using Conventional and Spectral GLM Analyses

Functional Magnetic Resonance Spectroscopy of Prolonged Motor Activation using Conventional and Spectral GLM Analyses

NMR in living systems

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