The correlation between white-matter microstructure and the complexity of spontaneous brain activity: a difussion tensor imaging-MEG study.
Fernández, A, Ríos-Lago, M, Abásolo, D, Hornero, R, Alvarez-Linera, J, Paul, N, Maestú, F and Ortiz, T (2011) The correlation between white-matter microstructure and the complexity of spontaneous brain activity: a difussion tensor imaging-MEG study. Neuroimage, 57 (4). pp. 1300-1307.
Fernandez et al_Neuroimage_final_version_2011.pdf - Accepted version Manuscript
Available under License : See the attached licence file.
The advent of new signal processing methods, such as non-linear analysis techniques, represents a new perspective which adds further value to brain signals' analysis. Particularly, Lempel-Ziv's Complexity (LZC) has proven to be useful in exploring the complexity of the brain electromagnetic activity. However, an important problem is the lack of knowledge about the physiological determinants of these measures. Although a correlation between complexity and connectivity has been proposed, this hypothesis was never tested in vivo. Thus, the correlation between the microstructure of the anatomic connectivity and the functional complexity of the brain needs to be inspected. In this study we analyzed the correlation between LZC and fractional anisotropy (FA), a scalar quantity derived from diffusion tensors that is particularly useful as an estimate of the functional integrity of myelinated axonal fibers, in a group of sixteen healthy adults (all female, mean age 65.56±6.06 years, intervals 58-82). Our results showed a positive correlation between FA and LZC scores in regions including clusters in the splenium of the corpus callosum, cingulum, parahipocampal regions and the sagittal stratum. This study supports the notion of a positive correlation between the functional complexity of the brain and the microstructure of its anatomical connectivity. Our investigation proved that a combination of neuroanatomical and neurophysiological techniques may shed some light on the underlying physiological determinants of brain's oscillations.
|Divisions :||Faculty of Engineering and Physical Sciences > Physics|
|Date :||15 August 2011|
|Identification Number :||https://doi.org/10.1016/j.neuroimage.2011.05.079|
|Additional Information :||NOTICE: this is the author’s version of a work that was accepted for publication in Neuroimage. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Neuroimage, 57(4), August 2011, DOI 10.1016/j.neuroimage.2011.05.079.|
|Depositing User :||Symplectic Elements|
|Date Deposited :||25 Jan 2012 01:29|
|Last Modified :||23 Sep 2013 19:02|
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