Fatigue in patients with hereditary neuropathy with liability to pressure palsies

Fritz, Nora; Chen, Yongsheng; Waters, L. A.; Saba, Sadaf; Hackett, Melody; Mada, Flicia; Li, Jun

doi:10.1002/acn3.51133

Cited by 4 publications

(7 citation statements)

References 24 publications

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“…Although volumetric muscle FF has been used as a monitoring biomarker in CMT1A, 11,15 its responsiveness may not be sufficient to show progression when FF is measured in early stage of diseases or over a short period 12,26 . Furthermore, the FF from a severely denervated muscle could be compromised by a “floor” effect, and would not be suitable for tracking the progression.…”

Section: Discussionmentioning

confidence: 99%

“…Two separate sessions were performed at thigh and calf. The scan sites were determined with bony markers and imaged using a comprehensive protocol for muscle and peripheral nerve as described 13,26 . There were 19 subjects scanned for both thigh and calf, three subjects only for calf, and 21 subjects only for thigh, leading to a total of 40 and 22 datasets for the thigh and calf.…”

Section: Methodsmentioning

confidence: 99%

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Automation of Quantifying Axonal Loss in Patients with Peripheral Neuropathies through Deep Learning Derived Muscle Fat Fraction

Chen

Moiseev

Kong

et al. 2021

Magnetic Resonance Imaging

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Background Axonal loss denervates muscle, leading to an increase of fat accumulation in the muscle. Therefore, fat fraction (FF) in whole limb muscle using MRI has emerged as a monitoring biomarker for axonal loss in patients with peripheral neuropathies. In this study, we are testing whether deep learning‐based model can automate quantification of the FF in individual muscles. While individual muscle is smaller with irregular shape, manually segmented muscle MRI images have been accumulated in this lab; and make the deep learning feasible. Purpose To automate segmentation on muscle MRI images through deep learning for quantifying individual muscle FF in patients with peripheral neuropathies. Study Type Retrospective. Subjects 24 patients and 19 healthy controls. Field Strength/Sequences 3T; Interleaved 3D GRE. Assessment A 3D U‐Net model was implemented in segmenting muscle MRI images. This was enabled by leveraging a large set of manually segmented muscle MRI images. B1+ and B1− maps were used to correct image inhomogeneity. Accuracy of the automation was evaluated using Pixel Accuracy (PA), Dice Coefficient (DC) in binary masks; and Bland‐Altman and Pearson correlation by comparing FF values between manual and automated methods. Statistical Tests PA and DC were reported with their median value and standard deviation. Two methods were compared using the ± 95% confidence intervals (CI) of Bland‐Altman analysis and the Pearson’s coefficient (r2). Results DC values were from 0.83 ± 0.17 to 0.98 ± 0.02 in thigh and from 0.63 ± 0.18 to 0.96 ± 0.02 in calf muscles. For FF values, the overall ± 95% CI and r2 were [0.49, –0.56] and 0.989 in thigh and [0.84, –0.71] and 0.971 in the calf. Data Conclusion Automated results well agreed with the manual results in quantifying FF for individual muscles. This method mitigates the formidable time consumption and intense labor in manual segmentations; and enables the use of individual muscle FF as outcome measures in upcoming longitudinal studies. Level of Evidence 3 Technical Efficacy Stage 1

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Automation of Quantifying Axonal Loss in Patients with Peripheral Neuropathies through Deep Learning Derived Muscle Fat Fraction

Chen

Moiseev

Kong

et al. 2021

Magnetic Resonance Imaging

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“…The increased tissue water content will lead to elevated T 1 , PD, and T 2 *. 15,22,23 In neural tissues, the T 1 is also associated with lipid-water interactions, that T 1 has been interpreted as a marker of demyelination and remyelination. 24 T 2 * is also sensitive to the magnetic field perturbation caused by the subject geometry and the paramagnetic or diamagnetic components of the local tissue.…”

Section: Discussionmentioning

confidence: 99%

“…T 1 , PD, and T 2 * are mainly associated with the tissue's total water content. The increased tissue water content will lead to elevated T 1 , PD, and T 2 * 15,22,23 . In neural tissues, the T 1 is also associated with lipid–water interactions, that T 1 has been interpreted as a marker of demyelination and remyelination 24 .…”

Section: Discussionmentioning

confidence: 99%

Multiparametric Quantitative MRI of Peripheral Nerves in the Leg: A Reliability Study

Chen

Baraz

Xuan

et al. 2023

Magnetic Resonance Imaging

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BackgroundPatients with polyneuropathies typically have demyelination and/or axonal degeneration in peripheral nerves. Currently, there is a lack of imaging biomarkers to track the changes in these pathologies.PurposeTo develop and evaluate the reliability of a multiparametric quantitative magnetic resonance imaging (qMRI) method of peripheral nerves in the leg.Study TypeProspective.SubjectsSeventeen healthy volunteers (36.2 ± 13.8 years old, 9 males) with 10 of them scanned twice for test–retest.Field Strength/Sequence3 T, three‐dimensional gradient echo and diffusion tensor imaging.AssessmentA qMRI protocol and processing pipeline was established for quantifying the following nerve parameters that are sensitive to myelin and axonal pathologies: magnetization transfer (MT) ratio (MTR), MT saturation index (MTsat), T2*, T1, proton density (PD), fractional anisotropy (FA), and mean/axial/radial diffusivities (MD, AD, and RD). The qMRI protocol also measures the volume of nerve fascicles (fVOL) and the fat fraction (FF) of muscles.Statistical TestsThe intersession reproducibility and inter‐rater reliability of each qMRI parameter were assessed by Bland–Altman analysis and intraclass correlation coefficient (ICC). Pairwise Pearson correlation analyses were performed to investigate the intrinsic association between qMRI parameters. Distal‐to‐proximal variations were evaluated by paired t‐tests with Bonferroni‐Holm multiple comparison corrections. P < 0.05 was considered statistically significant.ResultsThe MTR, MTsat, T2*, T1, PD, FA, AD, and fVOL of the sciatic and tibial nerves, and the FF of leg muscles, had an overall good‐to‐excellent test–retest agreement (ICC varying from 0.78 to 0.99). All the qMRI parameters had good‐to‐excellent inter‐rater reliability (ICC > 0.80). The data demonstrated a pattern of distal‐to‐proximal changes of an increased nerve MTsat and FA, and a decreased nerve T1, PD, MD, and RD, as well as a significantly increased muscle FF.Data ConclusionThe proposed multiparametric qMRI method of the peripheral nerves is highly reproducible and provided healthy control data which will be used in developing monitoring biomarkers in patients with polyneuropathies.Level of Evidence1Technical EfficacyStage 2

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“…Furthermore, concerns about offtarget effects, and the known side effects of ASOs including thrombocytopenia, could outweigh the benefit of treatment for a slowly progressive neuropathy such as CMT1A [156]. Finally, as maintaining the right degree of PMP-22 expression is critical to nerve health, excessive suppression could result in an HNPP phenotype, which is associated with patient morbidity comparable to that of CMT1A [157,158].…”

Section: Emerging Genetic Therapies In Cmt1amentioning

confidence: 99%

Mechanisms and Treatments in Demyelinating CMT

Fridman

Saporta

2021

Neurotherapeutics

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Demyelinating forms of Charcot-Marie-Tooth disease (CMT) are genetically and phenotypically heterogeneous and result from highly diverse biological mechanisms including gain of function (including dominant negative effects) and loss of function. While no definitive treatment is currently available, rapid advances in defining the pathomechanisms of demyelinating CMT have led to promising pre-clinical studies, as well as emerging clinical trials. Especially promising are the recently completed pre-clinical genetic therapy studies in PMP-22, GJB1, and SH3TC2-associated neuropathies, particularly given the success of similar approaches in humans with spinal muscular atrophy and transthyretin familial polyneuropathy. This article focuses on neuropathies related to mutations in PMP-22, MPZ, and GJB1, which together comprise the most common forms of demyelinating CMT, as well as on select rarer forms for which promising treatment targets have been identified. Clinical characteristics and pathomechanisms are reviewed in detail, with emphasis on therapeutically targetable biological pathways. Also discussed are the challenges facing the CMT research community in its efforts to advance the rapidly evolving biological insights to effective clinical trials. These considerations include the limitations of currently available animal models, the need for personalized medicine approaches/allele-specific interventions for select forms of demyelinating CMT, and the increasing demand for optimal clinical outcome assessments and objective biomarkers.

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Fatigue in patients with hereditary neuropathy with liability to pressure palsies

Cited by 4 publications

References 24 publications

Automation of Quantifying Axonal Loss in Patients with Peripheral Neuropathies through Deep Learning Derived Muscle Fat Fraction

Automation of Quantifying Axonal Loss in Patients with Peripheral Neuropathies through Deep Learning Derived Muscle Fat Fraction

Multiparametric Quantitative MRI of Peripheral Nerves in the Leg: A Reliability Study

Mechanisms and Treatments in Demyelinating CMT

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