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BackgroundMultiple system atrophy (MSA) is a fatal neurodegenerative disease characterized by the aggregation of α-synuclein in glia and neurons. Sirolimus (rapamycin) is an mTOR inhibitor that promotes α-synuclein autophagy and reduces its associated neurotoxicity in preclinical models.ObjectiveTo investigate the efficacy and safety of sirolimus in patients with MSA using a futility design. We also analyzed 1-year biomarker trajectories in the trial participants.MethodsRandomized, double-blind, parallel group, placebo-controlled clinical trial at the New York University of patients with probable MSA randomly assigned (3:1) to sirolimus (2-6 mg daily) for 48 weeks or placebo. Primary endpoint was change in the Unified MSA Rating Scale (UMSARS) total score from baseline to 48 weeks. (ClinicalTrials.gov NCT03589976).ResultsThe trial was stopped after a pre-planned interim analysis met futility criteria. Between August 15, 2018 and November 15, 2020, 54 participants were screened, and 47 enrolled and randomly assigned (35 sirolimus, 12 placebo). Of those randomized, 34 were included in the intention-to-treat analysis. There was no difference in change from baseline to week 48 between the sirolimus and placebo in UMSARS total score (mean difference, 2.66; 95% CI, -7.35-6.91; P = 0.648). There was no difference in UMSARS-1 and UMSARS-2 scores either. UMSARS scores changes were similar to those reported in natural history studies. Neuroimaging and blood biomarker results were similar in the sirolimus and placebo groups. Adverse events were more frequent with sirolimus. Analysis of 1-year biomarker trajectories in all participants showed that increases in blood neurofilament light chain (NfL) and reductions in whole brain volume correlated best with UMSARS progression.ConclusionsSirolimus for 48 weeks was futile to slow the progression of MSA and had no effect on biomarkers compared to placebo. One-year change in blood NfL and whole brain atrophy are promising biomarkers of disease progression for future clinical trials. © 2022 International Parkinson and Movement Disorder Society.

Purpose: Echo modulation curve (EMC) modeling enables accurate quantification of T2 relaxation times in multi‐echo spin‐echo (MESE) imaging. The standard EMC‐T2 mapping framework, however, requires sufficient echoes and cumbersome pixel‐wise dictionary‐matching steps. This work proposes a deep learning version of EMC‐T2 mapping, called DeepEMC‐T2mapping, to efficiently estimate accurate T2 maps from fewer echoes. Methods: DeepEMC‐T2 mapping was developed using a modified U‐Net to estimate both T2 and proton density (PD) maps directly from MESE images. The network implements several new features to improve the accuracy of T2/PD estimation. A total of 67 MESE datasets acquired in axial orientation were used for network training and evaluation. An additional 57 datasets acquired in coronal orientation with different scan parameters were used to evaluate the generalizability of the framework. The performance of DeepEMC‐T2 mapping was evaluated in seven experiments. Results: Compared to the reference, DeepEMC‐T2 mapping achieved T2 estimation errors from 1% to 11% and PD estimation errors from 0.4% to 1.5% with ten/seven/five/three echoes, which are more accurate than standard EMC‐T2 mapping. By incorporating datasets acquired with different scan parameters and orientations for joint training, DeepEMC‐T2 exhibits robust generalizability across varying imaging protocols. Increasing the echo spacing and including longer echoes improve the accuracy of parameter estimation. The new features proposed in DeepEMC‐T2 mapping all enabled more accurate T2 estimation. Conclusions: DeepEMC‐T2 mapping enables simplified, efficient, and accurate T2 quantification directly from MESE images without dictionary matching. Accurate T2 estimation from fewer echoes allows for increased volumetric coverage and/or higher slice resolution without prolonging total scan times. [ABSTRACT FROM AUTHOR]

Lower back pain is a common problem frequently encountered without specific biomarkers that correlate well with an individual patient's pain generators. MRI quantification of diffusion and T2 relaxation properties may provide novel insight into the mechanical and inflammatory changes that occur in the lumbosacral nerve roots in patients with lower back pain. Accurate imaging of the spinal nerve roots is difficult because of their small caliber and oblique course in all three planes. Two-dimensional in-plane imaging of the lumbosacral nerve roots requires oblique coronal imaging with large field of view (FOV) in both dimensions, resulting in severe geometric distortions using single-shot echo planar imaging (EPI) techniques. The present work describes initial success using a reduced-FOV single-shot spin-echo EPI acquisition to obtain in-plane diffusion tensor imaging (DTI) and T2 mapping of the bilateral lumbar nerve roots at the L4 level of healthy subjects, minimizing partial volume effects, breathing artifacts and geometric distortions. A significant variation in DTI and T2 mapping metrics is also reported along the course of the normal nerve root. The fractional anisotropy is statistically significantly lower in the dorsal root ganglia (0.287 ± 0.068) than in more distal regions in the spinal nerve (0.402 ± 0.040) (p

Purpose: To explore efficient encoding schemes for quantitative magnetization transfer (qMT) imaging with few constraints on model parameters. Theory and Methods: We combine two recently proposed models in a Bloch‐McConnell equation: the dynamics of the free spin pool are confined to the hybrid state, and the dynamics of the semi‐solid spin pool are described by the generalized Bloch model. We numerically optimize the flip angles and durations of a train of radio frequency pulses to enhance the encoding of three qMT parameters while accounting for all eight parameters of the two‐pool model. We sparsely sample each time frame along this spin dynamics with a three‐dimensional radial koosh‐ball trajectory, reconstruct the data with subspace modeling, and fit the qMT model with a neural network for computational efficiency. Results: We extracted qMT parameter maps of the whole brain with an effective resolution of 1.24 mm from a 12.6‐min scan. In lesions of multiple sclerosis subjects, we observe a decreased size of the semi‐solid spin pool and longer relaxation times, consistent with previous reports. Conclusion: The encoding power of the hybrid state, combined with regularized image reconstruction, and the accuracy of the generalized Bloch model provide an excellent basis for efficient quantitative magnetization transfer imaging with few constraints on model parameters. [ABSTRACT FROM AUTHOR]

Purpose: The accuracy of diffusion MRI tractography reconstruction decreases in the white matter regions with crossing fibers. The optic pathways in rodents provide a challenging structure to test new diffusion tractography approaches because of the small crossing volume within the optic chiasm and the unbalanced 9:1 proportion between the contra‐ and ipsilateral neural projections from the retina to the lateral geniculate nucleus, respectively. Methods: Common approaches based on Orientation Distribution Function (ODF) peak finding or statistical inference were compared qualitatively and quantitatively to ODF Fingerprinting (ODF‐FP) for reconstruction of crossing fibers within the optic chiasm using in vivo diffusion MRI (n=18$$ n=18 $$ healthy C57BL/6 mice). Manganese‐Enhanced MRI (MEMRI) was obtained after intravitreal injection of manganese chloride and used as a reference standard for the optic pathway anatomy. Results: ODF‐FP outperformed by over 100% all the tested methods in terms of the ratios between the contra‐ and ipsilateral segments of the reconstructed optic pathways as well as the spatial overlap between tractography and MEMRI. Conclusion: In this challenging model system, ODF‐Fingerprinting reduced uncertainty of diffusion tractography for complex structural formations of fiber bundles. [ABSTRACT FROM AUTHOR]