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

Pineda Pardo, José Ángel and Bruña Fernandez, Ricardo and Woolrich, Mark and Marcos, Alberto and Nobre, Anna Christina and Maestú, Fernando and 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.

Description

Title: Guiding functional connectivity estimation by structural connectivity in MEG: an application to discrimination of mild cognitive impaired conditions
Author/s:
  • Pineda Pardo, José Ángel
  • Bruña Fernandez, Ricardo
  • Woolrich, Mark
  • Marcos, Alberto
  • Nobre, Anna Christina
  • Maestú, Fernando
  • Vidaurre Henche, Diego
Item Type: Article
Título de Revista/Publicación: Neuroimage
Date: November 2014
ISSN: 1053-8119
Volume: 101
Subjects:
Freetext Keywords: Resting state; Diffusion tensor imaging; Magnetoencephalography; Multimodal neuroimaging; Multivariate sparse regression; Graphical Lasso; Mild cognitive impairment; Machine learning
Faculty: Centro de Tecnología Biomédica (CTB) (UPM)
Department: Aeronaves y Vehículos Espaciales
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

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.

Funding Projects

TypeCodeAcronymLeaderTitle
Government of SpainPSI2012-38375-C03-01UnspecifiedUnspecifiedUnspecified
Government of SpainPSI2009-14415-C03-01UnspecifiedUnspecifiedUnspecified
Government of SpainAP2010-1317UnspecifiedUnspecifiedUnspecified

More information

Item ID: 33515
DC Identifier: http://oa.upm.es/33515/
OAI Identifier: oai:oa.upm.es:33515
DOI: 10.1016/j.neuroimage.2014.08.002
Official URL: http://www.sciencedirect.com/science/article/pii/S1053811914006521
Deposited by: Memoria Investigacion
Deposited on: 21 Apr 2015 16:29
Last Modified: 06 May 2019 08:06
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