Observatorio de I+D+i UPM

Memorias de investigación
Research Publications in journals:
Guiding functional connectivity estimation by structural connectivity in MEG: An application to discrimination of Mild Cognitive Impaired conditions
Year:2014
Research Areas
  • Image processing
Information
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.
International
Si
JCR
Si
Title
Neuroimage
ISBN
1053-8119
Impact factor JCR
6,956
Impact info
Volume
101
10.1016/j.neuroimage.2014.08.002
Journal number
From page
765
To page
777
Month
SIN MES
Ranking
Participants
  • Autor: Jose Angel Pineda Pardo (UPM)
  • Autor: Ricardo Bruña Fernandez (UPM)
  • Autor: Mark Woolrich (University of Oxford)
  • Autor: Alberto Marcos (Hospital Clínico San Carlos)
  • Autor: Kia Nobre (University of Oxford)
  • Autor: Fernando Maestu Unturbe (UPM)
  • Autor: Diego Vidaurre Henche (UPM)
Research Group, Departaments and Institutes related
  • Creador: Centro o Instituto I+D+i: Centro de tecnología Biomédica CTB
  • Departamento: Tecnología Fotónica y Bioingeniería
S2i 2019 Observatorio de investigación @ UPM con la colaboración del Consejo Social UPM
Cofinanciación del MINECO en el marco del Programa INNCIDE 2011 (OTR-2011-0236)
Cofinanciación del MINECO en el marco del Programa INNPACTO (IPT-020000-2010-22)