Meditation-induced effects on whole-brain structural and effective connectivity

In the past decades, there has been a growing scientific interest in characterizing neural correlates of meditation training. Nonetheless, the mechanisms underlying meditation remain elusive. In the present work, we investigated meditation-related changes in functional dynamics and structural connectivity (SC). For this purpose, we scanned experienced meditators and control (naive) subjects using magnetic resonance imaging (MRI) to acquire structural and functional data during two conditions, resting-state and meditation (focused attention on breathing). In this way, we aimed to characterize and distinguish both short-term and long-term modifications in the brain's structure and function. First, to analyze the fMRI data, we calculated whole-brain effective connectivity (EC) estimates, relying on a dynamical network model to replicate BOLD signals' spatio-temporal structure, akin to functional connectivity (FC) with lagged correlations. We compared the estimated EC, FC, and SC links as features to train classifiers to predict behavioral conditions and group identity. Then, we performed a network-based analysis of anatomical connectivity. We demonstrated through a machine-learning approach that EC features were more informative than FC and SC solely. We showed that the most informative EC links that discriminated between meditators and controls involved several large-scale networks mainly within the left hemisphere. Moreover, we found that differences in the functional domain were reflected to a smaller extent in changes at the anatomical level as well. The network-based analysis of anatomical pathways revealed strengthened connectivity for meditators compared to controls between four areas in the left hemisphere belonging to the somatomotor, dorsal attention, subcortical and visual networks. Overall, the results of our whole-brain model-based approach revealed a mechanism underlying meditation by providing causal relationships at the structure-function level

Analysis of meiotic recombination in 22q11.2, a region that frequently undergoes deletions and duplications

BACKGROUND: The 22q11.2 deletion syndrome is the most frequent genomic disorder with an estimated frequency of 1/4000 live births. The majority of patients (90%) have the same deletion of 3 Mb (Typically Deleted Region, TDR) that results from aberrant recombination at meiosis between region specific low-copy repeats (LCRs). METHODS: As a first step towards the characterization of recombination rates and breakpoints within the 22q11.2 region we have constructed a high resolution recombination breakpoint map based on pedigree analysis and a population-based historical recombination map based on LD analysis. RESULTS: Our pedigree map allows the location of recombination breakpoints with a high resolution (potential recombination hotspots), and this approach has led to the identification of 5 breakpoint segments of 50 kb or less (8.6 kb the smallest), that coincide with historical hotspots. It has been suggested that aberrant recombination leading to deletion (and duplication) is caused by low rates of Allelic Homologous Recombination (AHR) within the affected region. However, recombination rate estimates for 22q11.2 region show that neither average recombination rates in the 22q11.2 region or within LCR22-2 (the LCR implicated in most deletions and duplications), are significantly below chromosome 22 averages. Furthermore, LCR22-2, the repeat most frequently implicated in rearrangements, is also the LCR22 with the highest levels of AHR. In addition, we find recombination events in the 22q11.2 region to cluster within families. Within this context, the same chromosome recombines twice in one family; first by AHR and in the next generation by NAHR resulting in an individual affected with the del22q11.2 syndrome. CONCLUSION: We show in the context of a first high resolution pedigree map of the 22q11.2 region that NAHR within LCR22 leading to duplications and deletions cannot be explained exclusively under a hypothesis of low AHR rates. In addition, we find that AHR recombination events cluster within families. If normal and aberrant recombination are mechanistically related, the fact that LCR22s undergo frequent AHR and that we find familial differences in recombination rates within the 22q11.2 region would have obvious health-related implications

Autonomous development of turn-taking behaviors in agent populations: a computational study

Comunicació presentada a 5th IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob); 2015 Aug 13-16; Providence, USA.We provide an original computational model showing how turn-taking behaviors can self-organize out of sensorimotor/ninteractions between vocalizing agents. These agents are equipped with a cognitive architecture based on two coupled/ncontrol loops: a reactive one implementing a basic regulatory behavior to maintain vocal listening and an adaptive one learning an action policy to maximize an overall group presence estimation. We show that the reactive process allows to bootstrap the adaptive learning to converge toward a collective turn-taking strategy. This model provides a computational support to the hypothesis that turn-taking can emerge from functional constraints related to group cohesion and vocal signal interferences and suggests future directions of research to understand how social behaviors/ncan result from sensorimotor interactions.This work is supported by the Socialising Sensori-Motor Contingencies project socSMC-641321H2020-FETPROACT-2014

Autonomous development of turn-taking behaviors in agent populations: a computational study

Comunicació presentada a 5th IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob); 2015 Aug 13-16; Providence, USA.We provide an original computational model showing how turn-taking behaviors can self-organize out of sensorimotor/ninteractions between vocalizing agents. These agents are equipped with a cognitive architecture based on two coupled/ncontrol loops: a reactive one implementing a basic regulatory behavior to maintain vocal listening and an adaptive one learning an action policy to maximize an overall group presence estimation. We show that the reactive process allows to bootstrap the adaptive learning to converge toward a collective turn-taking strategy. This model provides a computational support to the hypothesis that turn-taking can emerge from functional constraints related to group cohesion and vocal signal interferences and suggests future directions of research to understand how social behaviors/ncan result from sensorimotor interactions.This work is supported by the Socialising Sensori-Motor Contingencies project socSMC-641321H2020-FETPROACT-2014

Autonomous development of turn-taking behaviors in agent populations: a computational study

Comunicació presentada a 5th IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob); 2015 Aug 13-16; Providence, USA.We provide an original computational model showing how turn-taking behaviors can self-organize out of sensorimotor/ninteractions between vocalizing agents. These agents are equipped with a cognitive architecture based on two coupled/ncontrol loops: a reactive one implementing a basic regulatory behavior to maintain vocal listening and an adaptive one learning an action policy to maximize an overall group presence estimation. We show that the reactive process allows to bootstrap the adaptive learning to converge toward a collective turn-taking strategy. This model provides a computational support to the hypothesis that turn-taking can emerge from functional constraints related to group cohesion and vocal signal interferences and suggests future directions of research to understand how social behaviors/ncan result from sensorimotor interactions.This work is supported by the Socialising Sensori-Motor Contingencies project socSMC-641321H2020-FETPROACT-2014