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dc.contributor.authorSerrano, J. Ignacio
dc.contributor.authorRomero Muñoz, Juan Pablo 
dc.contributor.authorDel Castillo, María Dolores
dc.contributor.authorRocon, Eduardo
dc.contributor.authorLouis, Elan D.
dc.contributor.authorBenito León, Julián
dc.description.abstractEssential tremor (ET) is one of the most prevalent movement disorders. Being that it is a common disorder, its diagnosis is considered routine. However, misdiagnoses may occur regularly. Over the past decade, several studies have identified brain morphometric changes in ET, but these changes remain poorly understood. Here, we tested the informativeness of measuring cortical thickness for the purposes of ET diagnosis, applying feature selection and machine learning methods to a study sample of 18 patients with ET and 18 age- and sex-matched healthy control subjects. We found that cortical thickness features alone distinguished the two, ET from controls, with 81% diagnostic accuracy. More specifically, roughness (i.e., the standard deviation of cortical thickness) of the right inferior parietal and right fusiform areas was shown to play a key role in ET characterization. Moreover, these features allowed us to identify subgroups of ET patients as well as healthy subjects at risk for ET. Since treatment of tremors is disease specific, accurate and early diagnosis plays an important role in tremor management. Supporting the clinical diagnosis with novel computer approaches based on the objective evaluation of neuroimage data, like the one presented here, may represent a significant step in this direction.eng
dc.publisherNature. Scientific Reportsspa
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España*
dc.subjectEssential tremorspa
dc.subjectTemblor esencialspa
dc.titleA data mining approach using cortical thickness for diagnosis and characterization of essential
dc.description.extent1720 KBspa

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