Guiding functional connectivity estimation by structural connectivity in MEG: an application to discrimination of mild cognitive impaired conditions

Pineda Pardo, José Ángel; Bruña Fernandez, Ricardo; Woolrich, Mark; Marcos, Alberto; Nobre, Anna Christina; Maestú, Fernando y Vidaurre Henche, Diego (2014). Guiding functional connectivity estimation by structural connectivity in MEG: an application to discrimination of mild cognitive impaired conditions. "Neuroimage", v. 101 ; pp. 765-777. ISSN 1053-8119. https://doi.org/10.1016/j.neuroimage.2014.08.002.

Descripción

Título: Guiding functional connectivity estimation by structural connectivity in MEG: an application to discrimination of mild cognitive impaired conditions
Autor/es:
  • Pineda Pardo, José Ángel
  • Bruña Fernandez, Ricardo
  • Woolrich, Mark
  • Marcos, Alberto
  • Nobre, Anna Christina
  • Maestú, Fernando
  • Vidaurre Henche, Diego
Tipo de Documento: Artículo
Título de Revista/Publicación: Neuroimage
Fecha: Noviembre 2014
Volumen: 101
Materias:
Palabras Clave Informales: Resting state; Diffusion tensor imaging; Magnetoencephalography; Multimodal neuroimaging; Multivariate sparse regression; Graphical Lasso; Mild cognitive impairment; Machine learning
Escuela: Centro de Tecnología Biomédica (CTB) (UPM)
Departamento: Aeronaves y Vehículos Espaciales
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

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Resumen

Whole brain resting state connectivity is a promising biomarker that might help to obtain an early diagnosis in many neurological diseases, such as dementia. Inferring resting-state connectivity is often based on correlations, which are sensitive to indirect connections, leading to an inaccurate representation of the real backbone of the network. The precision matrix is a better representation for whole brain connectivity, as it considers only direct connections. The network structure can be estimated using the graphical lasso (GL), which achieves sparsity through l1-regularization on the precision matrix. In this paper, we propose a structural connectivity adaptive version of the GL, where weaker anatomical connections are represented as stronger penalties on the corre- sponding functional connections. We applied beamformer source reconstruction to the resting state MEG record- ings of 81 subjects, where 29 were healthy controls, 22 were single-domain amnestic Mild Cognitive Impaired (MCI), and 30 were multiple-domain amnestic MCI. An atlas-based anatomical parcellation of 66 regions was ob- tained for each subject, and time series were assigned to each of the regions. The fiber densities between the re- gions, obtained with deterministic tractography from diffusion-weighted MRI, were used to define the anatomical connectivity. Precision matrices were obtained with the region specific time series in five different frequency bands. We compared our method with the traditional GL and a functional adaptive version of the GL, in terms of log-likelihood and classification accuracies between the three groups. We conclude that introduc- ing an anatomical prior improves the expressivity of the model and, in most cases, leads to a better classification between groups.

Más información

ID de Registro: 33515
Identificador DC: http://oa.upm.es/33515/
Identificador OAI: oai:oa.upm.es:33515
Identificador DOI: 10.1016/j.neuroimage.2014.08.002
URL Oficial: http://www.sciencedirect.com/science/article/pii/S1053811914006521
Depositado por: Memoria Investigacion
Depositado el: 21 Abr 2015 16:29
Ultima Modificación: 01 Dic 2015 23:56
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