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

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  • José Angel Pineda-Pardo, Technical University of Madrid
  • ,
  • Ricardo Bruña, Technical University of Madrid
  • ,
  • Mark Woolrich, University of Oxford
  • ,
  • Alberto Marcos, University of Oxford, Complutense University of Madrid
  • ,
  • Anna C. Nobre, University of Oxford
  • ,
  • Fernando Maestú, Technical University of Madrid
  • ,
  • Diego Vidaurre

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 l 1 -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 corresponding functional connections. We applied beamformer source reconstruction to the resting state MEG recordings 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 obtained for each subject, and time series were assigned to each of the regions. The fiber densities between the regions, 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 introducing an anatomical prior improves the expressivity of the model and, in most cases, leads to a better classification between groups.

Original languageEnglish
Pages (from-to)765-777
Number of pages13
Publication statusPublished - 1 Nov 2014
Externally publishedYes

    Research areas

  • Diffusion tensor imaging, Graphical Lasso, Machine learning, Magnetoencephalography, Mild cognitive impairment, Multimodal neuroimaging, Multivariate sparse regression, Resting state

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