Johannes Forsting

Background: Complex regional pain syndrome (CRPS) is a disabling condition requiring long‐term treatment. This study evaluated long‐term outcomes in CRPS patients compared to posttraumatic neuralgia (PN) and nociceptive limb pain (NLP) 1–5 years after diagnosis and treatment initiation in a tertiary clinic. Methods: Patients with CRPS lasting more than 1 year ( n = 116), PN ( n = 68), or NLP ( n = 75) participated in a standardized telephone interview assessing pain intensity, functional impairment, neglect‐like symptoms, and disability in daily life and work. Results: PN patients had higher pain intensity compared to CRPS (NRS 5.7 ± 2.1 vs. 4.5 ± 2.6; main effect: p = 0.005; post hoc test: p = 0.003). Functional outcomes varied significantly between groups ( p < 0.001). CRPS patients showed the highest rate of severe impairment (71%), especially in those with ≥ 3 clinical signs, while outcome was better in NLP ( p ≤ 0.027), especially in part‐time workers. The global impression of disability varied between groups ( p = 0.044): PN patients reported a high level of severe disability (70%), while CRPS had the most subjects with “none/slight” disability (23%). Pain and function were tightly linked to perceived disability in all groups. Neither the neglect‐like total score (CRPS: 2.4 (2.2–2.6); neuralgia: 2.5 (2.3–2.7); NLP: 2.2 (1.9–2.5); p = 0.055) nor the working ability differed between groups ( p = 0.397); however, only in CRPS, neglect‐like symptoms were associated with higher pain intensity and global disability, as well as lower functional outcome. CRPS patients treated with corticosteroids ( n = 55) had more positive clinical signs at baseline and poorer outcomes than patients without corticosteroids ( n = 61). Conclusions: Long‐term impairment, work‐related, and global disability in CRPS were similar to those of neuralgia and NLP patients treated in a tertiary pain clinic. Despite shorter disease duration, CRPS patients more often report severe disability, while pain is comparable to other chronic limb pain courses. More pronounced positive signs in CRPS at baseline are associated with poor outcomes. These findings emphasize the heterogeneous course of CRPS and the importance of individualized multidimensional approaches.

Objectives: A tendency to jumping to conclusions (JTCs) was described in schizophrenia, in functional movement disorders and recently in a mixed group of chronic pain patients and has been discussed to indicate prefrontal dysfunction. This study investigated the tendency to premature decisions (JTC) in patients with complex regional pain syndrome (CRPS), a severe disorder affecting predominantly the distal limb, compared to healthy individuals and those with chronic limb pain from other causes, such as nerve injury or musculoskeletal abnormalities (non‐CRPS). Methods: In the classic ‘beads task’, visual stimuli were used to assess evidence‐based decision‐making ability, followed by a variation with somatosensory stimuli. Thirty patients with CRPS were compared to 23 non‐CRPS patients and 30 healthy individuals. Results were related to clinical data such as pain intensity, disease duration, CRPS phenotype based on predominant symptoms (central, peripheral and mixed) and standardized questionnaires evaluating depressive and anxiety symptoms. The main outcome was the number of draws to decision (DTD), i.e., how many beads participants requested before making a final judgement. Group differences were analysed using AN(C)OVA or Kruskal–Wallis tests with Bonferroni‐corrected post hoc comparisons, unpaired t ‐tests and chi‐squared tests as appropriate. Correlations between beads task performance and clinical parameters were examined using Pearson’s or Spearman’s analyses, while ANCOVA was applied to control for age, anxiety and depressive symptoms as covariates. Results: Both CRPS and non‐CRPS patients showed a significantly stronger JTC tendency, deciding at a lower DTD (2.63 ± 1.19 and 2.65 ± 1.27, respectively) than healthy controls (5.13 ± 1.92, both p < 0.001), without differences between patient groups, and the effect was independent of the CRPS phenotypes. DTD correlated with depressive or anxiety symptoms and age but not with pain intensity or disease duration. Conclusion: Dysfunctional processes leading to JTC seem to play a role in chronic pain patients and interact with depressive and anxiety symptoms. These interactions may impact treatment outcomes and warrant further investigation.

Background: Sporadic inclusion body myositis (sIBM) is the predominant idiopathic inflammatory myopathy (IIM) in older people. Limitations of classical clinical assessments have been discussed as possible explanations for failed clinical trials, underlining the need for more sensitive outcome measures. Quantitative muscle MRI (qMRI) is a promising candidate for evaluating and monitoring sIBM. Objective: Longitudinal assessment of qMRI in sIBM patients. Methods: We evaluated fifteen lower extremity muscles of 12 sIBM patients (5 females, mean age 69.6, BMI 27.8) and 12 healthy age- and gender-matched controls. Seven patients and matched controls underwent a follow-up evaluation after one year. Clinical assessment included testing for muscle strength with Quick Motor Function Measure (QMFM), IBM functional rating scale (IBM-FRS), and gait analysis (6-minute walking distance). 3T-MRI scans of the lower extremities were performed, including a Dixon-based sequence, T2 mapping and Diffusion Tensor Imaging. The qMRI-values fat-fraction (FF), water T2 relaxation time (wT2), fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity ( λ₁), and radial diffusivity (RD) were analysed. Results: Compared to healthy controls, significant differences for all qMRI parameters averaged over all muscles were found in sIBM using a MANOVA ( p < 0.001). In low-fat muscles (FF < 10%), a significant increase of wT2 and FA with an accompanying decrease of MD, λ₁, and RD was observed ( p≤0.020). The highest correlation with clinical assessments was found for wT2 values in thigh muscles ( r≤–0.634). Significant changes of FF (+3.0%), wT2 (+0.6 ms), MD (–0.04 10^-3mm²/s), λ₁ (–0.05 10^-3mm²/s), and RD (–0.03 10^-3mm²/s) were observed in the longitudinal evaluation of sIBM patients ( p≤0.001). FA showed no significant change ( p = 0.242). Conclusion: qMRI metrics correlate with clinical findings and can reflect different ongoing pathophysiological mechanisms. While wT2 is an emerging marker of disease activity, the role of diffusion metrics, possibly reflecting changes in fibre size and intracellular deposits, remains subject to further investigations.

Quantitative muscle magnetic resonance imaging (qMRI) is a valuable methodology for assessing muscular injuries and neuromuscular disorders. Notably, muscle diffusion tensor imaging (DTI) gives insights into muscle microstructural and macrostructural characteristics. However, the long‐term reproducibility and robustness of these measurements remain relatively unexplored. The purpose of this prospective longitudinal cohort study was to assess the long‐term robustness and range of variation of qMRI parameters, especially DTI metrics, in the lower extremity muscles of healthy controls under real‐life conditions. Twelve volunteers (seven females, age 44.1 ± 12.1 years, body mass index 23.3 ± 2.0 kg/m²) underwent five leg muscle MRI sessions every 20 ± 4 weeks over a total period of 1.5 years. A multiecho gradient‐echo Dixon‐based sequence, a multiecho spin‐echo T2‐mapping sequence, and a spin‐echo echo planar imaging diffusion‐weighted sequence were acquired bilaterally with a Philips 3‐T Achieva MR System using a 16‐channel torso coil. Fifteen leg muscles were segmented in both lower extremities. qMRI parameters, including fat fraction (FF), water T2 relaxation time, and the diffusion metrics fractional anisotropy (FA) and mean diffusivity (MD), were evaluated. Coefficients of variance (wsCV) and intraclass correlation coefficients (ICCs) were calculated to assess the reproducibility of qMRI parameters. The standard error of measurement (SEM) and the minimal detectable change (MDC) were calculated to determine the range of variation. All tests were applied to all muscles and, subsequently, to each muscle separately. wsCV showed good reproducibility (≤ 10%) for all qMRI parameters in all muscles. The ICCs revealed excellent agreement between time points (FF = 0.980, water T2 = 0.941, FA = 0.952, MD = 0.948). Random measurement errors assessed by SEM and the MDC were low (< 12%). In conclusion, in this study, we showed that qMRI parameters in healthy volunteers living normal lives are stable over 18 months, thereby defining a benchmark for the expected range of variation over time.

Limb‐girdle muscular dystrophy (LGMD) type R1 (LGMDR1) is the most common subtype of LGMD in Europe. Prospective longitudinal data, including clinical assessments and new biomarkers such as quantitative magnetic resonance imaging (qMRI), are needed to evaluate the natural course of the disease and therapeutic options. We evaluated eight thigh and seven leg muscles of 13 LGMDR1 patients (seven females, mean age 36.7 years, body mass index 23.9 kg/m²) and 13 healthy age‐ and gender‐matched controls in a prospective longitudinal design over 1 year. Clinical assessment included testing for muscle strength with quick motor function measure (QMFM), gait analysis and patient questionnaires (neuromuscular symptom score, activity limitation [ACTIVLIM]). MRI scans were performed on a 3‐T MRI scanner, including a Dixon‐based sequence, T2 mapping and diffusion tensor imaging. The qMRI values of fat fraction (FF), water T2 relaxation time (T2), fractional anisotropy, mean diffusivity, axial diffusivity and radial diffusivity were analysed. Within the clinical outcome measures, significant deterioration between baseline and follow‐up was found for ACTIVLIM (p = 0.029), QMFM (p = 0.012). Analysis of qMRI parameters of the patient group revealed differences between time points for both FF and T2 when analysing all muscles (FF: p < 0.001; T2: p = 0.016). The highest increase of fat replacement was found in muscles with an FF of between 10% and 50% at baseline. T2 in muscles with low‐fat replacement increased significantly. No significant differences were found for the diffusion metrics. Significant correlations between qMRI metrics and clinical assessments were found at baseline and follow‐up, while only T2 changes in thigh muscles correlated with changes in ACTIVLIM over time (ρ = −0.621, p < 0.05). Clinical assessments can show deterioration of the general condition of LGMDR1 patients. qMRI measures can give additional information about underlying pathophysiology. Further research is needed to establish qMRI outcome measures for clinical trials.

Background: Quantitative muscle MRI (qMRI) is a promising tool for evaluating and monitoring neuromuscular disorders (NMD). However, the application of different imaging protocols and processing pipelines restricts comparison between patient cohorts and disorders. In this qMRI study, we aim to compare dystrophic (limb-girdle muscular dystrophy), inflammatory (inclusion body myositis), and metabolic myopathy (Pompe disease) as well as patients with post-COVID-19 conditions suffering from myalgia to healthy controls. Methods: Ten subjects of each group underwent a 3T lower extremity muscle MRI, including a multi-echo, gradient-echo, Dixon-based sequence, a multi-echo, spin-echo (MESE) T2 mapping sequence, and a spin-echo EPI diffusion-weighted sequence. Furthermore, the following clinical assessments were performed: Quick Motor Function Measure, patient questionnaires for daily life activities, and 6-min walking distance. Results: Different involvement patterns of conspicuous qMRI parameters for different NMDs were observed. qMRI metrics correlated significantly with clinical assessments. Conclusions: qMRI metrics are suitable for evaluating patients with NMD since they show differences in muscular involvement in different NMDs and correlate with clinical assessments. Still, standardisation of acquisition and processing is needed for broad clinical use.

Muscle diffusion tensor imaging (mDTI)‐based tractography is a promising tool with which to detect subclinical changes in muscle injuries and to evaluate pathophysiology in neuromuscular diseases. Classic region of interest (ROI)‐based tractography is very time‐consuming and requires an examiner with extensive experience. (Semi)automatic approaches such as volume‐based tractography (VBT) can diminish this problem but its robustness and stability are unknown. The aim of the current study was to assess the performance of VBT in a multicenter setting and to evaluate semiautomatic segmentation approaches in the analysis of VBT‐derived data in terms of the comparability of the outcome measures. Five traveling volunteers underwent 3‐T mDTI of seven calf muscles of both legs at six different MR sites. Tract properties and diffusion metrics were calculated using VBT. Within‐subject coefficients of variance (wsCVs) and intraclass correlation coefficients (ICCs) were calculated to assess the multicenter reproducibility of tract properties such as tract density (TD), mean tract length, volume and tract propagation angle, and diffusion metrics such as fractional anisotropy, mean diffusivity, axial diffusivity (λ₁) and radial diffusivity in traveling subjects. Furthermore, 50 individual datasets from five different centers (10 datasets per center) were pooled to assess the feasibility of VBT with manual and semiautomatic segmentation. To assess the differences of tract properties and diffusion metrics between segmentation approaches an ANOVA was performed, and ICC and Bland–Altman plots were analyzed. wsCVs and ICCs showed good reproducibility of the tract properties TD and volume, as well as diffusion metrics. ANOVA showed no significant differences between manual and semiautomatic approaches. ICCs were excellent (≥ 0.992) and Bland–Altman analysis did not reveal any systemic bias between the methods. Tract properties and diffusion metrics derived from VBT showed good comparability among centers. Semiautomatic approaches revealed excellent agreement with gold standard of manual segmentation. These findings suggest that pooling data from different centers to construct a reference database for tractography results is feasible using semiautomatic segmentation approaches.

Quantitative MRI combines non-invasive imaging techniques to reveal alterations in muscle pathophysiology. Creating muscle-specific labels manually is time consuming and requires an experienced examiner. Semi-automatic and fully automatic methods reduce segmentation time significantly. Current machine learning solutions are commonly trained on data from healthy subjects using homogeneous databases with the same image contrast. While yielding high Dice scores (DS), those solutions are not applicable to different image contrasts and acquisitions. Therefore, the aim of our study was to evaluate the feasibility of automatic segmentation of a heterogeneous database. To create a heterogeneous dataset, we pooled lower leg muscle images from different studies with different contrasts and fields-of-view, containing healthy controls and diagnosed patients with various neuromuscular diseases. A second homogenous database with uniform contrasts was created as a subset of the first database. We trained three 3D-convolutional neuronal networks (CNN) on those databases to test performance as compared to manual segmentation. All networks, training on heterogeneous data, were able to predict seven muscles with a minimum average DS of 0.75. U-Net performed best when trained on the heterogeneous dataset (DS: 0.80 ± 0.10, AHD: 0.39 ± 0.35). ResNet and DenseNet yielded higher DS, when trained on a heterogeneous dataset (both DS: 0.86), as compared to a homogeneous dataset (ResNet DS: 0.83, DenseNet DS: 0.76). In conclusion, a CNN trained on a heterogeneous dataset achieves more accurate labels for predicting a heterogeneous database of lower leg muscles than a CNN trained on a homogenous dataset. We propose that a large heterogeneous database is needed, to make automated segmentation feasible for different kinds of image acquisitions.

Background: Muscle diffusion tensor imaging (mDTI) is a promising surrogate biomarker in the evaluation of muscular injuries and neuromuscular diseases. Since mDTI metrics are known to vary between different muscles, separation of different muscles is essential to achieve muscle-specific diffusion parameters. The commonly used technique to assess DTI metrics is parameter maps based on manual segmentation (MSB). Other techniques comprise tract-based approaches, which can be performed in a previously defined volume. This so-called volume-based tractography (VBT) may offer a more robust assessment of diffusion metrics and additional information about muscle architecture through tract properties. The purpose of this study was to assess DTI metrics of human calf muscles calculated with two segmentation techniques—MSB and VBT—regarding their inter-rater reliability in healthy and dystrophic calf muscles. Methods: 20 healthy controls and 18 individuals with different neuromuscular diseases underwent an MRI examination in a 3T scanner using a 16-channel Torso XL coil. DTI metrics were assessed in seven calf muscles using MSB and VBT. Coefficients of variation (CV) were calculated for both techniques. MSB and VBT were performed by two independent raters to assess inter-rater reliability by ICC analysis and Bland-Altman plots. Next to analysis of DTI metrics, the same assessments were also performed for tract properties extracted with VBT. Results: For both techniques, low CV were found for healthy controls (≤13%) and neuromuscular diseases (≤17%). Significant differences between methods were found for all diffusion metrics except for λ₁. High inter-rater reliability was found for both MSB and VBT (ICC ≥ 0.972). Assessment of tract properties revealed high inter-rater reliability (ICC ≥ 0.974). Conclusions: Both segmentation techniques can be used in the evaluation of DTI metrics in healthy controls and different NMD with low rater dependency and high precision but differ significantly from each other. Our findings underline that the same segmentation protocol must be used to ensure comparability of mDTI data.