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Quantifiable diagnosis of muscular dystrophies and neurogenic atrophies through network analysis

Aurora Sáez1, Eloy Rivas2, Adoración Montero-Sánchez3, Carmen Paradas345, Begoña Acha1, Alberto Pascual35, Carmen Serrano1 and Luis M Escudero35*

Author affiliations

1 Escuela Técnica Superior Ingeniería, Universidad de Sevilla, Seville, Spain

2 Department of Pathology, Hospital Universitario Virgen del Rocío, Seville, Spain

3 Instituto de Biomedicina de Sevilla, Hospital Universitario Virgen del Rocío/CSIC/Universidad de Sevilla, Seville, Spain

4 Neuromuscular Disease Unit, Neurology Department, Hospital Universitario Virgen del Rocío, Seville, Spain

5 Centro de Investigación Biomédica en Red de Enfermedades Neurodegenerativas (CIBERNED), Madrid, Spain

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Citation and License

BMC Medicine 2013, 11:77  doi:10.1186/1741-7015-11-77

Published: 20 March 2013



The diagnosis of neuromuscular diseases is strongly based on the histological characterization of muscle biopsies. However, this morphological analysis is mostly a subjective process and difficult to quantify. We have tested if network science can provide a novel framework to extract useful information from muscle biopsies, developing a novel method that analyzes muscle samples in an objective, automated, fast and precise manner.


Our database consisted of 102 muscle biopsy images from 70 individuals (including controls, patients with neurogenic atrophies and patients with muscular dystrophies). We used this to develop a new method, Neuromuscular DIseases Computerized Image Analysis (NDICIA), that uses network science analysis to capture the defining signature of muscle biopsy images. NDICIA characterizes muscle tissues by representing each image as a network, with fibers serving as nodes and fiber contacts as links.


After a ‘training’ phase with control and pathological biopsies, NDICIA was able to quantify the degree of pathology of each sample. We validated our method by comparing NDICIA quantification of the severity of muscular dystrophies with a pathologist’s evaluation of the degree of pathology, resulting in a strong correlation (R = 0.900, P <0.00001). Importantly, our approach can be used to quantify new images without the need for prior ‘training’. Therefore, we show that network science analysis captures the useful information contained in muscle biopsies, helping the diagnosis of muscular dystrophies and neurogenic atrophies.


Our novel network analysis approach will serve as a valuable tool for assessing the etiology of muscular dystrophies or neurogenic atrophies, and has the potential to quantify treatment outcomes in preclinical and clinical trials.

Computerized image analysis; Network theory; Neuromuscular disease; Systems biology