Validation of microsatellite markers for cytotype discrimination in the model grass Brachypodium distachyon

Brachypodium distachyon (2n = 2x = 10) is a small annual grass species where the existence of three different cytotypes (10, 20 and 30 chromosomes) has long been regarded as a case of autopolyploid series, with x = 5. However, it has been demonstrated that the cytotypes assumed to be polyploids represent two separate Brachypodium species recently named as B. stacei (2n = 2x = 20) and B. hybridum (2n = 4x = 30). The aim of this study was to find a PCR-based alternative approach that could replace standard cytotyping methods (i. e., chromosome counting and flow cytometry) to characterize each of the three Brachypodium species. We have analyzed with four microsatellite (SSR) markers eighty-three Brachypodium distachyon-type lines from varied locations in Spain, including the Balearic and Canary Islands. Within this set of lines, 64, 4 and 15 had 10, 20 and 30 chromosomes, respectively. The surveyed markers produced cytotype-specific SSR profiles. So, a single amplification product was generated in the diploid samples, with non-overlapping allelic ranges between the 2n = 10 and 2n = 20 cytotypes, whereas two bands, one in the size range of each of the diploid cytotypes, were amplified in the 2n = 30 lines. Furthermore, the remarkable size difference obtained with the SSR ALB165 allowed the identification of the Brachypodium species by simple agarose gel electrophoresis.

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

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.