Anomalous Consistency in Mild Cognitive Impairment: A Complex Networks Approach

Martínez Huartos, Johann Heinz; Ariza Bono, Pedro; Zanin, Massimiliano; Papo, David; Maestu Unturbe, Fernando; Pastor Ruiz, Juan Manuel; Bajo Breton, Ricardo; Boccaletti, Stefano y Martín Buldú, Javier (2015). Anomalous Consistency in Mild Cognitive Impairment: A Complex Networks Approach. "Chaos Solitons & Fractals", v. 70 ; pp. 144-155. ISSN 0960-0779. https://doi.org/10.1016/j.chaos.2014.10.013.

Descripción

Título: Anomalous Consistency in Mild Cognitive Impairment: A Complex Networks Approach
Autor/es:
  • Martínez Huartos, Johann Heinz
  • Ariza Bono, Pedro
  • Zanin, Massimiliano
  • Papo, David
  • Maestu Unturbe, Fernando
  • Pastor Ruiz, Juan Manuel
  • Bajo Breton, Ricardo
  • Boccaletti, Stefano
  • Martín Buldú, Javier
Tipo de Documento: Artículo
Título de Revista/Publicación: Chaos Solitons & Fractals
Fecha: Enero 2015
Volumen: 70
Materias:
Escuela: E.T.S.I. Agrónomos (UPM) [antigua denominación]
Departamento: Física y Mecánica Fundamental, Aplicada a la Ingeniería Agroforestal [hasta 2014]
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

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Resumen

Increased variability in performance has been associated with the emergence of several neurological and psychiatric pathologies. However, whether and how consistency of neuronal activity may also be indicative of an underlying pathology is still poorly understood. Here we propose a novel method for evaluating consistency from non-invasive brain recordings. We evaluate the consistency of the cortical activity recorded with magnetoencephalography in a group of subjects diagnosed with Mild Cognitive Impairment (MCI), a condition sometimes prodromal of dementia, during the execution of a memory task. We use metrics coming from nonlinear dynamics to evaluate the consistency of cortical regions. A representation known as parenclitic networks is constructed, where atypical features are endowed with a network structure, the topological properties of which can be studied at various scales. Pathological conditions correspond to strongly heterogeneous networks, whereas typical or normative conditions are characterized by sparsely connected networks with homogeneous nodes. The analysis of this kind of networks allows identifying the extent to which consistency is affected in the MCI group and the focal points where MCI is especially severe. To the best of our knowledge, these results represent the first attempt at evaluating the consistency of brain functional activity using complex networks theory.

Más información

ID de Registro: 38812
Identificador DC: http://oa.upm.es/38812/
Identificador OAI: oai:oa.upm.es:38812
Identificador DOI: 10.1016/j.chaos.2014.10.013
URL Oficial: http://www.sciencedirect.com/science/article/pii/S0960077914001842
Depositado por: Memoria Investigacion
Depositado el: 02 Feb 2016 16:10
Ultima Modificación: 31 Ene 2017 23:30
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