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OBJECTIVE: This study was undertaken to identify magnetic resonance (MR) metrics that are most sensitive to early changes in the brain in spinocerebellar ataxia type 1 (SCA1) and type 3 (SCA3) using an advanced multimodal MR imaging (MRI) protocol in the multisite trial setting. METHODS: SCA1 or SCA3 mutation carriers and controls (n = 107) underwent MR scanning in the US-European READISCA study to obtain structural, diffusion MRI, and MR spectroscopy data using an advanced protocol at 3T. Morphometric, microstructural, and neurochemical metrics were analyzed blinded to diagnosis and compared between preataxic SCA (n = 11 SCA1, n = 28 SCA3), ataxic SCA (n = 14 SCA1, n = 37 SCA3), and control (n = 17) groups using nonparametric testing accounting for multiple comparisons. MR metrics that were most sensitive to preataxic abnormalities were identified using receiver operating characteristic (ROC) analyses. RESULTS: Atrophy and microstructural damage in the brainstem and cerebellar peduncles and neurochemical abnormalities in the pons were prominent in both preataxic groups, when patients did not differ from controls clinically. MR metrics were strongly associated with ataxia symptoms, activities of daily living, and estimated ataxia duration. A neurochemical measure was the most sensitive metric to preataxic changes in SCA1 (ROC area under the curve [AUC] = 0.95), and a microstructural metric was the most sensitive metric to preataxic changes in SCA3 (AUC = 0.92). INTERPRETATION: Changes in cerebellar afferent and efferent pathways underlie the earliest symptoms of both SCAs. MR metrics collected with a harmonized advanced protocol in the multisite trial setting allow detection of disease effects in individuals before ataxia onset with potential clinical trial utility for subject stratification. ANN NEUROL 2023;93:686-701.

The article deals with a special method of measuring the maximum deviation of objects from roundness based on the inverse kinematics principle. The inverse measurement mechanism is based on the immobility of the measuring probes and the object performing all the motions required to measure a dimension. The advantage of this principle is minimization of the temperature change, while the adverse effect in the measurement system is greatly reduced at the same time. The measurement methodology requires a special software evaluation of the data measured. The aim of the given measurement methodology was to establish the maximum roundness deviation that corresponds to the Least Squares Circle (LSC) method. An experiment with three measuring probes was conducted to verify the methodology.

Purpose: To develop a 3D downfield (DF) MRSI protocol with whole brain coverage and post‐processing pipeline for creation of metabolite maps. Methods: A 3D, circularly phase‐encoded version of the previously developed 2D DF MRSI sequence with 13‾31‾$$ 1\overline{3}3\overline{1} $$ spectral‐spatial excitation and frequency selective refocusing was implemented and tested in five healthy volunteers at 3T. The DF metabolite maps with a nominal spatial resolution of 0.7 cm3 were recorded in eight slices at 3T in a scan time of 22 m 40 s. An MRSI post‐processing pipeline was developed to create DF metabolite maps. Metabolite concentrations and uncertainty estimates were compared between region differences for nine DF peaks. Results: LCModel analysis showed Cramer Rao lower bounds average values of 3%–4% for protein amide resonances in the three selected regions (anterior cingulate, dorsolateral prefrontal cortex, and centrum semiovale); Cramer Rao lower bounds were somewhat higher for individual peaks but for the most part were less than 20%. While DF concentration maps were visually quite homogeneous throughout the brain, general linear regression analysis corrected for multiple comparisons found significant differences between centrum semiovale and dorsolateral prefrontal cortex for peaks at 7.09 ppm (p = 0.014), 7.90 ppm (p = 0.009), 8.18 ppm (p = 0.009), combined amides (p = 0.009), and between anterior cingulate and dorsolateral prefrontal cortex for the 7.30 ppm peak (p = 0.020). Cramer Rao lower bounds values were not significantly different between brain regions for any of the DF peaks. Conclusion: The 3D DF MRSI of the human brain at 3T with wide spatial coverage for the mapping of exchangeable amide and other resonances is feasible at a nominal spatial resolution of 0.7 cm3. [ABSTRACT FROM AUTHOR]

Purpose: To develop an MRSI technique capable of mapping downfield proton resonances in the human brain. Methods: A spectral‐spatial excitation and frequency‐selective refocusing scheme, in combination with 2D phase encoding, was developed for mapping of downfield resonances without any perturbation of the water magnetization. An alternative scheme using spectral‐spatial refocusing was also investigated for simultaneous detection of both downfield and upfield resonances. The method was tested in 5 healthy human volunteers. Results: Downfield metabolite maps with a nominal spatial resolution of 1.5 cm3 were recorded at 3 T in a scan time of 12 minutes. Cramer‐Rao lower bounds for nine different downfield peaks were 20% or less over a single supraventricular slice. Downfield spectral profiles were similar to those in the literature recorded previously using single‐voxel localization methods. The same approach was also used for upfield MRSI, and simultaneous upfield and downfield acquisitions. Conclusion: The developed MRSI pulse sequence was shown to be an efficient way of rapidly mapping downfield resonances in the human brain at 3 T, maximizing sensitivity through the relaxation enhancement effect. Because the MRSI approach is efficient in terms of data collection and can be readily implemented at short TE, somewhat higher spatial resolution can be achieved than has been reported in previous single‐voxel downfield MRS studies. With this approach, nine downfield resonances could be mapped in a single slice for the first time using MRSI at 3 T. [ABSTRACT FROM AUTHOR]

Purpose: Fitting of MRS data plays an important role in the quantification of metabolite concentrations. Many different spectral fitting packages are used by the MRS community. A fitting challenge was set up to allow comparison of fitting methods on the basis of performance and robustness. Methods: Synthetic data were generated for 28 datasets. Short‐echo time PRESS spectra were simulated using ideal pulses for the common metabolites at mostly near‐normal brain concentrations. Macromolecular contributions were also included. Modulations of signal‐to‐noise ratio (SNR); lineshape type and width; concentrations of γ‐aminobutyric acid, glutathione, and macromolecules; and inclusion of artifacts and lipid signals to mimic tumor spectra were included as challenges to be coped with. Results: Twenty‐six submissions were evaluated. Visually, most fit packages performed well with mostly noise‐like residuals. However, striking differences in fit performance were found with bias problems also evident for well‐known packages. In addition, often error bounds were not appropriately estimated and deduced confidence limits misleading. Soft constraints as used in LCModel were found to substantially influence the fitting results and their dependence on SNR. Conclusions: Substantial differences were found for accuracy and precision of fit results obtained by the multiple fit packages. [ABSTRACT FROM AUTHOR]

The neurochemical information provided by proton magnetic resonance spectroscopy (MRS) or MR spectroscopic imaging (MRSI) can be severely compromised if strong signals originating from brain water and extracranial lipids are not properly suppressed. The authors of this paper present an overview of advanced water/lipid‐suppression techniques and describe their advantages and disadvantages. Moreover, they provide recommendations for choosing the most appropriate techniques for proper use. Methods of water signal handling are primarily focused on the VAPOR technique and on MRS without water suppression (metabolite cycling). The section on lipid‐suppression methods in MRSI is divided into three parts. First, lipid‐suppression techniques that can be implemented on most clinical MR scanners (volume preselection, outer‐volume suppression, selective lipid suppression) are described. Second, lipid‐suppression techniques utilizing the combination of k‐space filtering, high spatial resolutions and lipid regularization are presented. Finally, three promising new lipid‐suppression techniques, which require special hardware (a multi‐channel transmit system for dynamic B1+ shimming, a dedicated second‐order gradient system or an outer volume crusher coil) are introduced. [ABSTRACT FROM AUTHOR]

Proton MR spectra of the brain, especially those measured at short and intermediate echo times, contain signals from mobile macromolecules (MM). A description of the main MM is provided in this consensus paper. These broad peaks of MM underlie the narrower peaks of metabolites and often complicate their quantification but they also may have potential importance as biomarkers in specific diseases. Thus, separation of broad MM signals from low molecular weight metabolites enables accurate determination of metabolite concentrations and is of primary interest in many studies. Other studies attempt to understand the origin of the MM spectrum, to decompose it into individual spectral regions or peaks and to use the components of the MM spectrum as markers of various physiological or pathological conditions in biomedical research or clinical practice. The aim of this consensus paper is to provide an overview and some recommendations on how to handle the MM signals in different types of studies together with a list of open issues in the field, which are all summarized at the end of the paper. [ABSTRACT FROM AUTHOR]

Purpose: A properly characterized macromolecular (MM) contribution is essential for accurate metabolite quantification in FID‐MRSI. MM information can be included into the fitting model as a single component or parameterized and included over several individual MM resonances, which adds flexibility when pathologic changes are present but is prone to potential overfitting. This study investigates the effects of different MM models on MRSI reproducibility. Methods: Clinically feasible, high‐resolution FID‐MRSI data were collected in ~5 min at 7 Tesla from 10 healthy volunteers and quantified via LCModel (version 6.3) with 3 basis sets, each with a different approach for how the MM signal was handled: averaged measured whole spectrum (full MM), 9 parameterized components (param MM) with soft constraints to avoid overparameterization, or without any MM information included in the fitting prior knowledge. The test–retest reproducibility of MRSI scans was assessed voxel‐wise using metabolite coefficients of variation and intraclass correlation coefficients and compared between the basis sets. Correlations of concentration estimates were investigated for the param MM fitting model. Results: The full MM model provided the most reproducible quantification of total NAA, total Cho, myo‐inositol, and glutamate + glutamine ratios to total Cr (coefficients of variations ≤ 8%, intraclass correlation coefficients ≥ 0.76). Using the param MM model resulted in slightly lower reproducibility (up to +3% higher coefficients of variations, up to −0.1 decreased intraclass correlation coefficients). The quantification of the parameterized macromolecules did not affect quantification of the overlapping metabolites. Conclusion: Clinically feasible FID‐MRSI with an experimentally acquired MM spectrum included in prior knowledge provides highly reproducible quantification for the most common neurometabolites in healthy volunteers. Parameterization of the MM spectrum may be preferred as a compromise between quantification accuracy and reproducibility when the MM content is expected to be pathologically altered. [ABSTRACT FROM AUTHOR]