Biology subjects

Deco, G.

Publications and source records attributed to Deco, G..

At least 109 records · Page 6Linked to original sources

bioRxiv2022

Single session of rTMS enhances brain metastability and intrinsic ignition

BackgroundEmerging evidence support the view that brain stimulation might improve essential tremor (ET) by altering brain networks and facilitating plasticity. Yet, we are still missing a mechanistic explanation of the whole brain dynamics underlying these plasticity defining changes. MethodIn this study, we explored the effect of low-frequency repetitive transcranial magnetic stimulation (rTMS) over left primary motor cortex (L-M1) on functional connectivity dynamics (FCD) in patients with ET. Resting-state fMRI (RsfMRI) was acquired before and after a single session of rTMS in 30 patients with ET and compared with RsfMRI of 20 age and gender matched healthy controls (HCs). We have measured the effect of brain stimulation using network topological re-organization through whole brain integration and segregation, brain stability and capacity of neural propagation through metastability and intrinsic ignition. ResultsPatients with ET had altered FCD measures compared to controls. After a single session rTMS, the brain connectivity measures approached normality and patients with ET revealed significantly higher integration, lower segregation with higher metastability and increased intrinsic ignition. ConclusionBrain metastability and intrinsic ignition measures could be valuable tools in appreciating mechanisms of brain stimulation in ET and other neurological diseases.

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bioRxiv2022

Data-driven discovery of canonical large-scale brain dynamics

Human behavior and cognitive function correlate with complex patterns of spatio-temporal brain dynamics, which can be simulated using computational models with different degrees of biophysical realism. We used a data-driven optimization algorithm to determine and classify the types of local dynamics that enable the reproduction of different observables derived from functional magnetic resonance recordings. The phase space analysis of the resulting equations revealed a predominance of stable spiral attractors, which optimized the similarity with the empirical data in terms of the synchronization, metastability, and functional connectivity dynamics. For stable limit cycles, departures from harmonic oscillations improved the fit in terms of functional connectivity dynamics. Eigenvalue analyses showed that the proximity to Hopf bifurcations improved the accuracy of the simulation for wakefulness, while deep sleep was associated with increased stability. Our results provide testable predictions that constrain the landscape of suitable biophysical models, while supporting noise-driven dynamics close to a bifurcation as a canonical mechanism underlying the complex fluctuations that characterize endogenous brain activity.

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bioRxiv2022

A physical neural mass model framework for the analysis of oscillatory generators from laminar electrophysiological recordings

Cortical function emerges from the interactions of multi-scale networks that may be studied at a high level using neural mass models (NMM), representing the mean activity of large numbers of neurons. In order to properly reproduce experimental data, these models require the addition of further elements. Here we provide a framework integrating conduction physics that can be used to simulate cortical electrophysiology measurements, particularly those obtained from multi-contact laminar electrodes. This is achieved by endowing NMMs with basic physical properties, such as the average laminar location of the apical and basal dendrites of pyramidal cell populations. We call this framework laminar NMM, or LaNMM for short. We then employ this framework to infer the location of oscillatory generators from laminar-resolved data collected from the prefrontal cortex in the macaque monkey. Based on the literature on columnar connectivity, we define a minimal neural mass model capable of generating amplitude and phase coupled slow (alpha/beta, 4-22 Hz) and fast (gamma, 30-250 Hz) oscillations. The synapse layer locations of the two pyramidal cell populations are treated as optimization parameters, together with two more LaNMM-specific parameters, to compare the models with the multi-contact recordings. We rank the candidate models using an optimization function that evaluates the match between the functional connectivity (FC) of the model and data, where the FC is defined by the covariance between bipolar voltage measurements at different cortical depths. The family of best solutions reproduces the FC of the observed electrophysiology while selecting locations of pyramidal cells and their synapses that result in the generation of fast activity at superficial layers and slow activity across most depths, in line with recent literature proposals. HighlightsO_LIWe provide a neural mass modeling formalism that includes a physical layer to simulate electrophysiology measurements. C_LIO_LITo analyze in-vivo data collected in the macaque monkey during a memory task, we propose a specific model with two coupled main circuits that can generate realistic electrophysiological signals in two important oscillatory regimes--the alpha/beta and the gamma bands. C_LIO_LIPhysical elements in the model shed light on the generation of oscillations in the two regimes and on the relative power distribution of fast and slow oscillatory signals across cortical depth, which we show can be altered by the choice of the reference location or method. C_LIO_LIThe model is contrasted with in-vivo data, with parameters adjusted by matching voltage statistics in the alpha/beta and gamma bands, leading to a solution with slow frequency components generated by synapses spanning most cortical layers and fast oscillations in superficial layers. C_LIO_LIThe resulting formalism provides useful tools and concepts to analyze and model data, with implications for understanding altered oscillatory EEG activity in dementia, Alzheimers disease and other disorders with oscillatory features. C_LI

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bioRxiv2022

Brain dynamics predictive of response to psilocybin for treatment-resistant depression

Psilocybin therapy for depression has started to show promise, yet the underlying causal mechanisms are not currently known. Here we leveraged the differential outcome in responders and non-responders to psilocybin (10mg and 25mg, 7 days apart) therapy for depression - to gain new insights into regions and networks implicated in the restoration of healthy brain dynamics. We used whole-brain modelling to fit the spatiotemporal brain dynamics at rest in both responders and non-responders before treatment. Dynamic sensitivity analysis of systematic perturbation of these models enabled us to identify specific brain regions implicated in a transition from a depressive brain state to a heathy one. Binarizing the sample into treatment responders (>50% reduction in depressive symptoms) versus non-responders enabled us to identify a subset of regions implicated in this change. Interestingly, these regions correlate with in vivo density maps of serotonin receptors 5-HT2A and 5-HT1A, which psilocin, the active metabolite of psilocybin, has an appreciable affinity for, and where it acts as a full-to-partial agonist. Serotonergic transmission has long been associated with depression and our findings provide causal mechanistic evidence for the role of brain regions in the recovery from depression via psilocybin.

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bioRxiv2022

Neural mass modelling for the masses: Democratising access to whole-brain biophysical modelling with FastDMF

Different whole-brain models constrained by neuroimaging data have been developed during the last years to investigate causal hypotheses related to brain mechanisms. Among these, the Dynamic Mean Field (DMF) model is a particularly attractive model, combining a biophysically realistic single-neuron model that is scaled up via a mean-field approach and multimodal imaging data. Despite these favourable features, an important barrier for a widespread usage of the DMF model is that current implementations are computationally expensive - to the extent that the model often becomes unfeasible when no high-performance computing infrastructure is available. Furthermore, even when such computing structure is available, current implementations can only support simulations on brain parcellations that consider less than 100 brain regions. To remove these barriers, here we introduce a user-friendly and computationally-efficient implementation of the DMF model, which we call FastDMF, with the goal of making biophysical whole-brain modelling accessible to neuroscientists worldwide. By leveraging a suit of analytical and numerical advances - including a novel estimation of the feedback inhibition control parameter, and a Bayesian optimisation algorithm - the FastDMF circumvents various computational bottlenecks of previous implementations. An evaluation of the performance of the FastDMF showed that it can attain a significantly faster performance than previous implementations while reducing the memory consumption by several orders of magnitude. Thanks to these computational advances, FastDMF makes it possible to increase the number of simulated regions by one order of magnitude: we found good agreement between empirical and simulated functional MRI data parcellated at two different spatial scales (N=90 and N=1000 brain regions). These advances open the way to the widespread use of biophysically grounded whole-brain models for understanding the interplay among anatomy, function and brain dynamics in health and disease, and to provide mechanistic explanations of recent results obtained from empirical fine-grained neuroimaging data sets, such as turbulence or connectome harmonics. HighlightsO_LIWe present the FastDMF, a user-friendly and computationally efficient implementation of the Dynamic Mean Field model for simulations of whole-brain dynamics. C_LIO_LIUsing analytical and numerical tools, we develop a novel estimation of the feedback inhibition control based on the structural connectivity, bypassing an important computational bottleneck. C_LIO_LIThe FastDMF is coupled with a Bayesian Optimization algorithm significantly reducing the number of simulations required to fit the FastDMF to empirical neuroimaging data. C_LIO_LIOur advances open the possibility of simulating thousands of brain regions in a biophysically grounded whole-brain model. C_LI

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bioRxiv2022

Synchronization in the connectome: Metastable oscillatory modes emerge from interactions in the brain spacetime network

A rich repertoire of oscillatory signals is detected from human brains with electro- and magnetoencephalography (EEG/MEG). However, the principles underwriting coherent oscillations and their link with neural activity remain under debate. Here, we revisit the mechanistic hypothesis that transient brain rhythms are a signature of metastable synchronization, occurring at reduced collective frequencies due to delays between brain areas. We consider a system of damped oscillators in the presence of background noise - approximating the short-lived gamma-frequency oscillations generated within neuronal circuits - coupled according to the diffusion weighted tractography between brain areas. Varying the global coupling strength and conduction speed, we identify a critical regime where spatially and spectrally resolved metastable oscillatory modes (MOMs) emerge at sub-gamma frequencies, approximating the MEG power spectra from 89 healthy individuals at rest. Further, we demonstrate that the frequency, duration, and scale of MOMs - as well as the frequency-specific envelope functional connectivity - can be controlled by global parameters, while the connectome structure remains unchanged. Grounded in the physics of delay-coupled oscillators, these numerical analyses demonstrate how interactions between locally generated fast oscillations in the connectome spacetime structure can lead to the emergence of collective brain rhythms organized in space and time.

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bioRxiv2021

Disruption in structural-functional network repertoire and time resolved subcortical-frontoparietal connectivity in disorders of consciousness

Understanding recovery of consciousness and elucidating its underlying mechanism is believed to be crucial in the field of basic neuroscience and medicine. Ideas such as the global neuronal workspace and the mesocircuit theory hypothesize that failure of recovery in conscious states coincide with loss of connectivity between subcortical and frontoparietal areas, a loss of the repertoire of functional networks states and metastable brain activation. We adopted a time-resolved functional connectivity framework to explore these ideas and assessed the repertoire of functional network states as a potential marker of consciousness and its potential ability to tell apart patients in the unresponsive wakefulness syndrome (UWS) and minimally conscious state (MCS). In addition, prediction of these functional network states by underlying hidden spatial patterns in the anatomical network, i.e. so-called eigenmodes, were supplemented as potential markers. By analysing time-resolved functional connectivity from fMRI data, we demonstrated a reduction of metastability and functional network repertoire in UWS compared to MCS patients. This was expressed in terms of diminished dwell times and loss of nonstationarity in the default mode network and fronto-parietal subcortical network in UWS compared to MCS patients. We further demonstrated that these findings co-occurred with a loss of dynamic interplay between structural eigenmodes and emerging time-resolved functional connectivity in UWS. These results are, amongst others, in support of the global neuronal workspace theory and the mesocircuit hypothesis, underpinning the role of time-resolved thalamo-cortical connections and metastability in the recovery of consciousness.

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bioRxiv2021

The gendered self: Evidence for differences in whole-brain dynamics

How the brain constructs gender identity is largely unknown, but some neural differences have recently been discovered. Here, we used an intrinsic-ignition framework to investigate if gender identity changes the propagation of the neural activity across the whole-brain network and within resting-state networks. Studying 29 transmen and 17 transwomen with gender incongruence, 22 ciswomen, and 19 cismen, we computed the capability of a given brain area in space to propagate activity to other areas (mean-ignition) and its variability across time (node-metastability). We found that both measures differentiated all four groups across the whole-brain network. Furthermore, at the network level, we found that compared to the other groups, cismen showed higher mean-ignition of the dorsal attention network and node-metastability of the dorsal and ventral attention, executive control, and temporal parietal networks. We also found mean-ignition differences between cismen and ciswomen within the executive control network, but higher in ciswomen than cismen and transmen for the default-mode network. For the node-metastability, this was higher in cismen compared to ciswomen in the somatomotor network, while both mean-ignition and node-metastability were higher for cismen than transmen in the limbic network. Finally, we computed correlations between both measures and their body image scores. Transmen dissatisfaction, cismen, and ciswomen satisfaction towards their own body image were distinctively associated with specific networks per group. Overall, the study of the whole-brain network dynamical complexity discriminates binary gender identity groups, and functional connectivity dynamics approaches are needed to disentangle the complex understanding of the gendered self. Significance statementThe study of sex/gender differences may be enriched by the heterogeneity of other gender minority groups, such as transgender. Functional connectivity measures capturing the spatio-temporal oscillations of the brain can provide insights on how the brain cooperates. This is the first study investigating how the whole-brain network propagates information across the brain, spatially and temporally, in binary gender groups (cisgender and transgender) by means of the intrinsic-ignition framework. We found four whole-brain unique phenotypes pertaining to each gender group, namely cismen, ciswomen, transmen and transwomen. Novel functional connectivity dynamics frameworks can contribute to disentangle the complex experience of a gendered-self.

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bioRxiv2021

The effect of external stimulation on functional networks in the aging healthy human brain

Understanding the brain changes occurring during aging can provide new insights for developing treatments that alleviate or reverse cognitive decline. Neurostimulation techniques have emerged as potential treatments for brain disorders and to improve cognitive functions. Nevertheless, given the ethical restrictions of neurostimulation approaches, in silico perturbation protocols based on causal whole-brain models are fundamental to gaining a mechanistic understanding of brain dynamics. Furthermore, this strategy could serve as a more specific biomarker relating local activity with global brain dynamics. Here, we used a large resting-state fMRI dataset divided into middle-aged (N=310, aged < 65 years) and older adults (N=310, aged[≥] 65) to characterize brain states in each group as a probabilistic metastable substate (PMS) space, each with a probabilistic occurrence and frequency. Then, we fitted the PMS to a whole-brain model and applied in silico stimulations with different intensities in each node to force transitions from the brain states of the older group to the middle-age group. We found that the precuneus, a brain area belonging to the default mode network and the rich club, was the best stimulation target. These findings might have important implications for designing neurostimulation interventions to revert the effects of aging on whole-brain dynamics.

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bioRxiv2021

Integrated resource for functional and structural connectivity of the marmoset brain

Comprehensive integration of structural and functional connectivity data is required to model brain functions accurately. While resources for studying the structural connectivity of non-human primate brains already exist, their integration with functional connectivity data has remained unavailable. Here we present a comprehensive resource that integrates the most extensive awake marmoset resting-state fMRI data available to date (39 marmoset monkeys, 710 runs, 12117 mins) with previously published cellular-level neuronal tracing (52 marmoset monkeys, 143 injections) and multi-resolution diffusion MRI datasets. The combination of these data allowed us to (1) map the fine-detailed functional brain networks and cortical parcellations; (2) develop a deep-learning-based parcellation generator that preserves the topographical organization of functional connectivity and reflects individual variabilities, and (3) investigate the structural basis underlying functional connectivity by computational modeling. This resource will enable modeling structure-function relationships and facilitate future comparative and translational studies of primate brains.

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bioRxiv2021

Posterior integration and thalamo-frontotemporal broadcasting are impaired in disorders of consciousness

The study of the brains static and dynamical activity is opening a valuable source of assistance for the clinical assessment of patients with disorders of consciousness. For example, glucose uptake and dysfunctional spread of naturalistic and synthetic stimuli has proven useful to characterize hampered consciousness. However, understanding of the mechanisms behind loss of consciousness following brain injury is still missing. Here, we study the propagation of endogenous and in-silico exogenous perturbations in patients with disorders of consciousness, based upon directed and causal interactions estimated from resting-state fMRI. We found that patients with disorders of consciousness suffer decreased capacity for neural propagation and responsiveness to events, and that this can be related to glucose metabolism as measured with [18F]FDG-PET. In particular, we show that loss of consciousness is related to the malfunctioning of two neural circuits: the posterior cortical regions failing to convey information, in conjunction with reduced broadcasting of information from subcortical, temporal, parietal and frontal regions. These results seed light on the mechanisms behind disorders of consciousness, triangulating network function with basic measures of brain integrity and behavior. HighlightsO_LIPropagation of neural events and network responses are disrupted in patients with DoC. C_LIO_LILoss of consciousness is related to the malfunctioning of two neural circuits. C_LIO_LIPosterior cortical regions lack to integrate information in altered consciousness. C_LIO_LIBreakdown of information broadcasting of subcortical cortical areas in DoC. C_LIO_LILoss of network responses in DoC patients is related to glucose metabolism. C_LI

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bioRxiv2021

The 2D Ising model, criticality and AIT

In this short note we study the 2D Ising model, a universal computational model which reflects phase transitions and critical phenomena, as a framework for establishing links between systems that exhibit criticality with the notions of complexity. This is motivated in the context of neuroscience applications stemming from algorithmic information theory (AIT). Starting with the original 2D Ising model, we show that -- together with correlation length of the spin lattice, susceptibility to a uniform external field -- the correlation time of the magnetization time series, the compression ratio of the spin lattice, the complexity of the magnetization time series -- as derived from Lempel-Ziv-Welch compression--, and the rate of information transmission in the lattice, all reflect the effects of the phase transition, which results in spacetime pockets of uniform magnetization at all scales. We also show that in the Ising model the insertion of sparse long-range couplings has a direct effect on the critical temperature and other parameters. The addition of positive links extends the ordered regime to higher critical temperatures, while negative links have a stronger, disordering influence at the global scale. We discuss some implications for the study of long-range (e.g., ephaptic) interactions in the human brain and the effects of weak perturbations in neural dynamics.

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bioRxiv2021

Temporal pattern recognition in the human brain: a dual simultaneous processing

Pattern recognition is a major scientific topic. Strikingly, while machine learning algorithms are constantly refined, the human brain emerges as an ancestral biological example of such complex procedure. However, how it transforms sequences of single objects into meaningful temporal patterns remains elusive. Using magnetoencephalography (MEG) and magnetic resonance imaging (MRI), we discovered and mathematically modelled an inedited dual simultaneous processing responsible for pattern recognition in the brain. Indeed, while the objects of the temporal pattern were independently elaborated by a local, rapid brain processing, their combination into a meaningful superordinate pattern depended on a concurrent global, slower processing involving a widespread network of sequentially active brain areas. Expanding the established knowledge of neural information flow from low- to high-order brain areas, we revealed a novel brain mechanism based on simultaneous activity in different frequency bands within the same brain regions, highlighting its crucial role underlying complex cognitive functions.

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bioRxiv2021

Unifying turbulent dynamics framework distinguishes different brain states

Recently, significant advances have been made by identifying the levels of synchronicity of the underlying dynamics of a given brain state. This research has demonstrated that unconscious dynamics tend to be more synchronous than those found in conscious states, which are more asynchronous. Here we go beyond this dichotomy to demonstrate that the different brain states are always underpinned by spatiotemporal chaos but with dissociable turbulent dynamics. We investigated human neuroimaging data from different brain states (resting state, meditation, deep sleep, and disorders of consciousness after coma) and were able to distinguish between them using complementary model-free and model-based measures of turbulent information transmission. Our model-free approach used recent advances describing a measure of information cascade across spatial scales using tools from turbulence theory. Complementarily, our model-based approach used exhaustive in silico perturbations of whole-brain models fitted to the empirical neuroimaging data, which allowed us to study the information encoding capabilities of the brain states. Overall, the current framework demonstrates that different levels of turbulent dynamics are fundamental for describing and differentiating between brain states.

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bioRxiv2021

On the edge of criticality: strength-dependent perturbation unveils delicate balance between fluctuation and oscillation in brain dynamics

Despite decades of research, there is still a lack of understanding of the role and generating mechanisms of the ubiquitous fluctuations and oscillations found in recordings of brain dynamics. Here, we used a strength-dependent perturbative framework to provide a causal mechanistic description of how human brain function is perched at the delicate balance between fluctuation and oscillation. Applying local strength-dependent perturbations and subsequently measuring the perturbative complexity index clearly demonstrates that the overall balance of brain dynamics is shifted towards fluctuations for providing much needed flexibility. Importantly, stimulation in the fluctuation regime modulates specific resting state network, thus providing a mechanistic explanation of experimentally reported brain dynamics. Furthermore, this framework generates specific, testable empirical predictions for human stimulation studies using strength-dependent rather than constant perturbation. Overall, the strength-dependent perturbative framework demonstrates how the human brain is poised on the edge of criticality, between fluctuations to oscillations, allowing for maximal flexibility.

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bioRxiv2021

Temporal irreversibility of neural dynamics as a signature of consciousness

Even though the fundamental laws of physics are the same when the direction of time is inverted, dissipative systems evolve in the preferred temporal direction indicated by the thermodynamic arrow of time. The fundamental nature of this temporal asymmetry led us to hypothesize its presence in the neural activity evoked by conscious perception of the physical world, and thus its covariance with the level of conscious awareness. Inspired by recent developments in stochastic thermodynamics, we implemented a data-driven and model-free deep learning framework to decode the temporal inversion of electrocorticography signals acquired from non-human primates. Brain activity time series recorded during conscious wakefulness could be distinguished from their inverted counterparts with high accuracy, both using frequency and phase information. However, classification accuracy was reduced for data acquired during deep sleep and under ketamine-induced anesthesia; moreover, the predictions obtained from multiple independent neural networks were less consistent for sleep and anesthesia than for conscious wakefulness. Finally, the analysis of feature importance scores highlighted transitions between slow ({approx}20 Hz) and fast frequencies (> 40 Hz) as the main contributors to the temporal asymmetry observed during conscious wakefulness. Our results show that a preferred temporal direction is simultaneously manifest in the neural activity evoked by conscious mentation and in the phenomenology of the passage of time, establishing common ground to tackle the relationship between brain and subjective experience.

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bioRxiv2021

Assessing the influence of local neural activity on global connectivity fluctuations: Application to human intracranial EEG during a cognitive task

Cognitive-relevant information is processed by different brain areas that cooperate to eventually produce a response. The relationship between local activity and global brain states during such processes, however, remains for the most part unexplored. To address this question, we designed a simple face-recognition task performed in patients with drug-resistant epilepsy and monitored with intracranial EEG. Based on our observations, we developed a novel analytical framework (named "local-global" framework) to statistically correlate the brain activity in every recorded gray-matter region with the widespread connectivity fluctuations as proxy to identify concurrent local activations and global brain phenomena that may plausibly reflect a common functional network during cognition. The application of the local-global framework to the data from 3 subjects showed that similar connectivity fluctuations found across patients were mainly coupled to the local activity of brain areas involved in face information processing. In particular, our findings provide preliminary evidence that the reported global measures might be a novel signature of functional brain activity reorganization when a stimulus is processed in a task context regardless of the specific recorded areas. Data availability statementDue to institutional restrictions, the data that supports the findings of this study can be accessed only with a data sharing agreement. All code used in this work can be found at https://github.com/mvilavidal/localglobal2022. Funding statementMVV was supported by a fellowship from "la Caixa" Foundation, Spain (ID 100010434, fellowship code LCF/BQ/DE17/11600022). MVV and ATC were supported by the Bial Foundation grant 106/18. GD and ATC were supported by the project "Cluster Emergent del Cervell Huma" (CECH, ref. 001-P-001682), within the framework of the European Research Development Fund Operational Program of Catalonia 2014-2020. GD was supported by a Spanish national research project (ref. PID2019-105772GB-I00 MCIU AEI) funded by the Spanish Ministry of Science, Innovation and Universities (MCIU), State Research Agency (AEI); HBP SGA3 Human Brain Project Specific Grant Agreement 3 (grant agreement no. 945539), funded by the EU H2020 FET Flagship programme; SGR Research Support Group support (ref. 2017 SGR 1545), funded by the Catalan Agency for Management of University and Research Grants (AGAUR); Neurotwin Digital twins for model-driven non-invasive electrical brain stimulation (grant agreement ID: 101017716) funded by the EU H2020 FET Proactive programme; euSNN European School of Network Neuroscience (grant agreement ID: 860563) funded by the EU H2020 MSCA-ITN Innovative Training Networks; Brain-Connects: Brain Connectivity during Stroke Recovery and Rehabilitation (id. 201725.33) funded by the Fundacio La Marato TV3; Corticity, FLAG-ERA JTC 2017, (ref. PCI2018-092891) funded by the Spanish Ministry of Science, Innovation and Universities (MCIU), State Research Agency (AEI). Conflict of interest disclosureThe authors declare no conflicts of interest. Ethics approval statementThe study was conducted in accordance with the Declaration of Helsinki. All diagnostic, surgical and experimental procedures have been previously approved by The Clinical Ethical Committee of Hospital Clinic (Barcelona, Spain). In particular, the specific proposal to run the cognitive experiments for this study was approved in March 2020 under the code number HCB/2020/0182. Patient consent statementInformed consent was explicitly obtained from all participants prior to the recordings and the performance of the tasks.

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bioRxiv2021

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 structural and functional connectivities (SC and FC, respectively). 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 brains structure and function. First, we performed a network-based analysis of anatomical connectivity. Then, 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 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. The whole-brain SC analysis revealed strengthened anatomical connectivity across large-scale networks for meditators compared to controls. We found that differences in SC were reflected in the functional domain as well. We demonstrated through a machine-learning approach that EC features were more informative than FC and SC solely. Using EC features we reached high performance for the condition-based classification within each group and moderately high accuracies when comparing the two groups in each condition. Moreover, we showed that the most informative EC links that discriminated between meditators and controls involved the same large-scale networks previously found to have increased anatomical connectivity. Overall, the results of our whole-brain model-based approach revealed a mechanism underlying meditation by providing causal relationships at the structure-function level.

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