Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10609/123166
Título : Modified connectivity of vulnerable brain nodes in multiple sclerosis, their impact on cognition and their discriminative value
Autoría: Solana, Elisabeth  
Martínez de las Heras, Eloy
Casas-Roma, Jordi  
Calvet Liñán, Laura  
Lopez-Soley, Elisabet  
Sepúlveda, Maria
Sola-Valls, Nuria  
Montejo, Carmen
Blanco, Yolanda
Pulido Valdeolivas, Irene
Andorrà, Magí  
Saiz, Albert  
Prados Carrasco, Ferran  
Llufriu, Sara  
Otros: University College London (UCL)
Universitat de Barcelona (UB)
Universitat Oberta de Catalunya (UOC)
Citación : Solana, E., Martinez-Heras, E., Casas-Roma, J., Calvet, L., Lopez-Soley, E., Sepulveda, M., Solà-Valls, N., Montejo, C., Blanco, Y., Pulido-Valdeolivas, I., Andorra, M., Saiz, A., Prados, F. & Llufriu, S. (2019). Modified connectivity of vulnerable brain nodes in multiple sclerosis, their impact on cognition and their discriminative value. Scientific Reports, 9(), 1-8. doi: 10.1038/s41598-019-56806-z
Resumen : Brain structural network modifications in multiple sclerosis (MS) seem to be clinically relevant. The discriminative ability of those changes to identify MS patients or their cognitive status remains unknown. Therefore, this study aimed to investigate connectivity changes in MS patients related to their cognitive status, and to define an automatic classification method to classify subjects as patients and healthy volunteers (HV) or as cognitively preserved (CP) and impaired (CI) patients. We analysed structural brain connectivity in 45 HV and 188 MS patients (104 CP and 84 CI). A support vector machine with k-fold cross-validation was built using the graph metrics features that best differentiate the groups (p<0.05). Local efficiency (LE) and node strength (NS) network properties showed the largest differences: 100% and 69.7% of nodes had reduced LE and NS in CP patients compared to HV. Moreover, 55.3% and 57.9% of nodes had decreased LE and NS in CI compared to CP patients, in associative multimodal areas. The classification method achieved an accuracy of 74.8-77.2% to differentiate patients from HV, and 59.9-60.8% to discriminate CI from CP patients. Structural network integrity is widely reduced and worsens as cognitive function declines. Central network properties of vulnerable nodes can be useful to classify MS patients.
Palabras clave : esclerosis múltiple
conectividad cerebral estructural
imagen de resonancia magnética
DOI: 10.1038/s41598-019-56806-z
Tipo de documento: info:eu-repo/semantics/article
Versión del documento: info:eu-repo/semantics/publishedVersion
Fecha de publicación : 27-dic-2019
Licencia de publicación: http://creativecommons.org/licenses/by/3.0/es/  
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