Guiding functional connectivity estimation by structural connectivity in MEG

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Subtitle: an application to discrimination of conditions of mild cognitive impairment
Author list: Maestu F
Publisher: Elsevier
Publication year: 2014
Volume number: 101
Start page: 765
End page: 777
Number of pages: 13
ISSN: 1053-8119
Languages: English-Great Britain (EN-GB)


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 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, inmost
cases, leads to a better classification between groups.


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Last updated on 2019-13-08 at 00:45