Author: Eleonora De Filippi#1, Anira Escrichs#2, Estela Càmara3,4, César Garrido5, Theo Marins6, Marti Sánchez-Fibla7, Matthieu Gilson2,8, Gustavo Deco2,9,10,11
1 Computational Neuroscience Group, Center for Brain and Cognition, Department of Information and Communication Technologies, Universitat Pompeu Fabra, C Ramon Trias Fargas, 25-27, 08005, Barcelona, Catalonia, Spain. email@example.com.
2 Computational Neuroscience Group, Center for Brain and Cognition, Department of Information and Communication Technologies, Universitat Pompeu Fabra, C Ramon Trias Fargas, 25-27, 08005, Barcelona, Catalonia, Spain.
3 Cognition and Brain Plasticity Unit, Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet de Llobregat, Barcelona, Spain.
4 Department of Cognition, Development and Educational Psychology, University of Barcelona, Barcelona, Spain.
5 Radiology Unit, Hospital Clínic Barcelona, Barcelona, Spain.
6 D'Or Institute for Research and Education (IDOR), Rio de Janeiro, RJ, Brazil.
7 Department of Information and Communications Technologies, Universitat Pompeu Fabra, 08018, Barcelona, Spain.
8 Theory of Multi-scale Neuronal Networks, INM-6, Forschungszentrum Juelich, Jülich, Germany.
9 Institució Catalana de la Recerca i Estudis Avançats (ICREA), Barcelona, Catalonia, Spain.
10 Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.
11 Turner Institute for Brain and Mental Health, Monash University, Melbourne, VIC, Australia.
Conference/Journal: Brain Struct Funct
Date published: 2022 May 6
Other: Special Notes: doi: 10.1007/s00429-022-02496-9. , Word Count: 286
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.
Keywords: Effective connectivity; Meditation; Resting-state; Structural connectivity; Whole-brain modeling; fMRI.
PMID: 35524072 DOI: 10.1007/s00429-022-02496-9