Dataset Information

Muscle MRI in patients with dysferlinopathy: pattern recognition and implications for clinical trials.

ABSTRACT: BACKGROUND AND OBJECTIVE:Dysferlinopathies are a group of muscle disorders caused by mutations in the DYSF gene. Previous muscle imaging studies describe a selective pattern of muscle involvement in smaller patient cohorts, but a large imaging study across the entire spectrum of the dysferlinopathies had not been performed and previous imaging findings were not correlated with functional tests. METHODS:We present cross-sectional T1-weighted muscle MRI data from 182 patients with genetically confirmed dysferlinopathies. We have analysed the pattern of muscles involved in the disease using hierarchical analysis and presented it as heatmaps. Results of the MRI scans have been correlated with relevant functional tests for each region of the body analysed. RESULTS:In 181 of the 182 patients scanned, we observed muscle pathology on T1-weighted images, with the gastrocnemius medialis and the soleus being the most commonly affected muscles. A similar pattern of involvement was identified in most patients regardless of their clinical presentation. Increased muscle pathology on MRI correlated positively with disease duration and functional impairment. CONCLUSIONS:The information generated by this study is of high diagnostic value and important for clinical trial development. We have been able to describe a pattern that can be considered as characteristic of dysferlinopathy. We have defined the natural history of the disease from a radiological point of view. These results enabled the identification of the most relevant regions of interest for quantitative MRI in longitudinal studies, such as clinical trials. CLINICAL TRIAL REGISTRATION:NCT01676077.

SUBMITTER: Diaz-Manera J

PROVIDER: S-EPMC6166612 | biostudies-literature | 2018 Oct

REPOSITORIES: biostudies-literature

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Muscle MRI in patients with dysferlinopathy: pattern recognition and implications for clinical trials.

Diaz-Manera Jordi J Fernandez-Torron Roberto R LLauger Jaume J James Meredith K MK Mayhew Anna A Smith Fiona E FE Moore Ursula R UR Blamire Andrew M AM Carlier Pierre G PG Rufibach Laura L Mittal Plavi P Eagle Michelle M Jacobs Marni M Hodgson Tim T Wallace Dorothy D Ward Louise L Smith Mark M Stramare Roberto R Rampado Alessandro A Sato Noriko N Tamaru Takeshi T Harwick Bruce B Rico Gala Susana S Turk Suna S Coppenrath Eva M EM Foster Glenn G Bendahan David D Le Fur Yann Y Fricke Stanley T ST Otero Hansel H Foster Sheryl L SL Peduto Anthony A Sawyer Anne Marie AM Hilsden Heather H Lochmuller Hanns H Grieben Ulrike U Spuler Simone S Tesi Rocha Carolina C Day John W JW Jones Kristi J KJ Bharucha-Goebel Diana X DX Salort-Campana Emmanuelle E Harms Matthew M Pestronk Alan A Krause Sabine S Schreiber-Katz Olivia O Walter Maggie C MC Paradas Carmen C Hogrel Jean-Yves JY Stojkovic Tanya T Takeda Shin'ichi S Mori-Yoshimura Madoka M Bravver Elena E Sparks Susan S Bello Luca L Semplicini Claudio C Pegoraro Elena E Mendell Jerry R JR Bushby Kate K Straub Volker V

Journal of neurology, neurosurgery, and psychiatry 20180507 10

<h4>Background and objective</h4>Dysferlinopathies are a group of muscle disorders caused by mutations in the <i>DYSF</i> gene. Previous muscle imaging studies describe a selective pattern of muscle involvement in smaller patient cohorts, but a large imaging study across the entire spectrum of the dysferlinopathies had not been performed and previous imaging findings were not correlated with functional tests.<h4>Methods</h4>We present cross-sectional T1-weighted muscle MRI data from 182 patients ...[more]

PMID: 29735511

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Project description:We explored the performance of structure-based computational analysis in four neurodegenerative conditions [Ataxia (AT, n = 16), Huntington's Disease (HD, n = 52), Alzheimer's Disease (AD, n = 66), and Primary Progressive Aphasia (PPA, n = 50)], all characterized by brain atrophy. The independent variables were the volumes of 283 anatomical areas, derived from automated segmentation of T1-high resolution brain MRIs. The segmentation based volumetric quantification reduces image dimensionality from the voxel level [on the order of [Formula: see text](106)] to anatomical structures [[Formula: see text](102)] for subsequent statistical analysis. We evaluated the effectiveness of this approach on extracting anatomical features, already described by human experience and a priori biological knowledge, in specific scenarios: (1) when pathologies were relatively homogeneous, with evident image alterations (e.g., AT); (2) when the time course was highly correlated with the anatomical changes (e.g., HD), an analogy for prediction; (3) when the pathology embraced heterogeneous phenotypes (e.g., AD) so the classification was less efficient but, in compensation, anatomical and clinical information were less redundant; and (4) when the entity was composed of multiple subgroups that had some degree of anatomical representation (e.g., PPA), showing the potential of this method for the clustering of more homogeneous phenotypes that can be of clinical importance. Using the structure-based quantification and simple linear classifiers (partial least square), we achieve 87.5 and 73% of accuracy on differentiating AT and pre-symptomatic HD patents from controls, respectively. More importantly, the anatomical features automatically revealed by the classifiers agreed with the patterns previously described on these pathologies. The accuracy was lower (68%) on differentiating AD from controls, as AD does not display a clear anatomical phenotype. On the other hand, the method identified PPA clinical phenotypes and their respective anatomical signatures. Although most of the data are presented here as proof of concept in simulated clinical scenarios, structure-based analysis was potentially effective in characterizing phenotypes, retrieving relevant anatomical features, predicting prognosis, and aiding diagnosis, with the advantage of being easily translatable to clinics and understandable biologically.

Dataset Information

Muscle MRI in patients with dysferlinopathy: pattern recognition and implications for clinical trials.

Publications

Muscle MRI in patients with dysferlinopathy: pattern recognition and implications for clinical trials.

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