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Descripción

Computational neuroscience models have been used for understanding neural dynamics in the brain and how they may be altered when physiological or other conditions change. We review and develop a data-driven approach to neuroimaging data called the energy landscape analysis. The methods are rooted in statistical physics theory, in particular the Ising model, also known as the (pairwise) maximum entropy model and Boltzmann machine. The methods have been applied to fitting electrophysiological data in neuroscience for a decade, but their use in neuroimaging data is still in its infancy. We first review the methods and discuss some algorithms and technical aspects. Then, we apply the methods to functional magnetic resonance imaging data recorded from healthy individuals to inspect the relationship between the accuracy of fitting, the size of the brain system to be analysed and the data length.This article is part of the themed issue 'Mathematical methods in medicine: neuroscience, cardiology and pathology'.

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UCL University College London Eprints  

Autor(es)

Ezaki, T -  Watanabe, T -  Ohzeki, M -  Masuda, N - 

Id.: 69877673

Versión: 1.0

Estado: Final

Palabras claveBoltzmann machine, Ising model, functional magnetic resonance imaging, statistical physics - 

Tipo de recurso: Article  - 

Tipo de Interactividad: Expositivo

Nivel de Interactividad: muy bajo

Audiencia: Estudiante  -  Profesor  -  Autor  - 

Estructura: Atomic

Coste: no

Copyright: sí

Requerimientos técnicos:  Browser: Any - 

Relación: [IsBasedOn] Philos Trans A Math Phys Eng Sci , 375 (2096) (2017)

Fecha de contribución: 29-may-2017

Contacto:

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