scholarly journals MOU-EC: model-based whole-brain effective connectivity to extract biomarkers for brain dynamics from fMRI data and study distributed cognition

2019 ◽  
Author(s):  
M Gilson ◽  
G Zamora-López ◽  
V Pallarés ◽  
MH Adhikari ◽  
M Senden ◽  
...  
Keyword(s):  
Distributed Cognition ◽  
Dynamic Models ◽  
Effective Connectivity ◽  
Statistical Testing ◽  
Fmri Data ◽  
Brain Areas ◽  
Whole Brain ◽  
Model Based ◽  
The Brain ◽  
Connectivity Measure

AbstractNeuroimaging techniques are increasingly used to study brain cognition in humans. Beyond their individual activation, the functional associations between brain areas have become a standard proxy to describe how information is distributed across the brain network. Among the many analysis tools available, dynamic models of brain activity have been developed to overcome the limitations of original connectivity measures such as functional connectivity. In particular, much effort has been devoted to the assessment of directional interactions between brain areas from their observed activity. This paper summarizes our recent approach to analyze fMRI data based on our whole-brain effective connectivity referred to as MOU-EC, while discussing the pros and cons of its underlying assumptions with respect to other established approaches. Once tuned, the model provides a connectivity measure that reflects the dynamical state of BOLD activity obtained using fMRI, which can be used to explore the brain cognition. We focus on two important applications. First, as a connectivity measure, MOU-EC can be used to extract biomarkers for task-specific brain coordination, understood as the patterns of areas exchanging information. The multivariate nature of connectivity measures raises several challenges for whole-brain analysis, for which machine-learning tools presents some advantages over statistical testing. Second, we show how to interpret changes in MOU-EC connections in a collective and model-based manner, bridging with network analysis. To illustrate our framework, we use a dataset where subjects were recorded in two conditions, watching a movie and a black screen (referred to as rest). Our framework provides a comprehensive set of tools that open exciting perspectives for the study of distributed cognition, as well as neuropathologies.

scholarly journals Model-based whole-brain effective connectivity to study distributed cognition in health and disease

2020 ◽  
Vol 4 (2) ◽  
pp. 338-373 ◽  
Author(s):  
Matthieu Gilson ◽  
Gorka Zamora-López ◽  
Vicente Pallarés ◽  
Mohit H. Adhikari ◽  
Mario Senden ◽  
...  
Keyword(s):  
Distributed Cognition ◽  
Dynamic Models ◽  
Effective Connectivity ◽  
Statistical Testing ◽  
Human Cognition ◽  
Fmri Data ◽  
Brain Areas ◽  
Whole Brain ◽  
Model Based ◽  
The Many

Neuroimaging techniques are now widely used to study human cognition. The functional associations between brain areas have become a standard proxy to describe how cognitive processes are distributed across the brain network. Among the many analysis tools available, dynamic models of brain activity have been developed to overcome the limitations of original connectivity measures such as functional connectivity. This goes in line with the many efforts devoted to the assessment of directional interactions between brain areas from the observed neuroimaging activity. This opinion article provides an overview of our model-based whole-brain effective connectivity to analyze fMRI data, while discussing the pros and cons of our approach with respect to other established approaches. Our framework relies on the multivariate Ornstein-Uhlenbeck (MOU) process and is thus referred to as MOU-EC. Once tuned, the model provides a directed connectivity estimate that reflects the dynamical state of BOLD activity, which can be used to explore cognition. We illustrate this approach using two applications on task-evoked fMRI data. First, as a connectivity measure, MOU-EC can be used to extract biomarkers for task-specific brain coordination, understood as the patterns of areas exchanging information. The multivariate nature of connectivity measures raises several challenges for whole-brain analysis, for which machine-learning tools present some advantages over statistical testing. Second, we show how to interpret changes in MOU-EC connections in a collective and model-based manner, bridging with network analysis. Our framework provides a comprehensive set of tools that open exciting perspectives to study distributed cognition, as well as neuropathologies.

scholarly journals Leveling up: improving power in fMRI by moving beyond cluster-level inference

2021 ◽  
Author(s):  
Stephanie Noble ◽  
Mandy Mejia ◽  
Andrew Zalesky ◽  
Dustin Scheinost
Keyword(s):  
Average Effect ◽  
Brain Areas ◽  
Whole Brain ◽  
Typical Sample ◽  
Human Connectome Project ◽  
Inference Network ◽  
The Brain ◽  
Cluster Level ◽  
Human Neuroimaging ◽  
Adequate Power

Inference in neuroimaging commonly occurs at the level of "clusters" of neighboring voxels or connections, thought to reflect functionally specific brain areas. Yet increasingly large studies reveal effects that are shared throughout the brain, suggesting that reported clusters may only reflect the "tip of the iceberg" of underlying effects. Here, we empirically compare power of traditional levels of inference (edge and cluster) with broader levels of inference (network and whole-brain) by resampling functional connectivity data from the Human Connectome Project (n=40, 80, 120). Only network- and whole brain-level inference attained or surpassed "adequate" power (β =80%) to detect an average effect, with almost double the power for network- compared with cluster-level procedures at more typical sample sizes. Likewise, effects tended to be widespread, and more widespread pooling resulted in stronger magnitude effects. Power also substantially increased when controlling FDR rather than FWER. Importantly, there may be similar implications for task-based activation analyses where effects are also increasingly understood to be widespread. However, increased power with broader levels of inference may diminish the specificity to localize effects, especially for non-task contexts. These findings underscore the benefit of shifting the scale of inference to better capture the underlying signal, which may unlock opportunities for discovery in human neuroimaging.

scholarly journals Sparse DCM for whole-brain effective connectivity from resting-state fMRI data

NeuroImage ◽  
2020 ◽  
Vol 208 ◽  
pp. 116367 ◽  
Author(s):  
Giulia Prando ◽  
Mattia Zorzi ◽  
Alessandra Bertoldo ◽  
Maurizio Corbetta ◽  
Marco Zorzi ◽  
...  
Keyword(s):  
Resting State ◽  
Effective Connectivity ◽  
Resting State Fmri ◽  
Fmri Data ◽  
Whole Brain

scholarly journals Quantification of Effective Connectivity in the Brain Using a Measure of Directed Information

2012 ◽  
Vol 2012 ◽  
pp. 1-16 ◽  
Author(s):  
Ying Liu ◽  
Selin Aviyente
Keyword(s):  
Granger Causality ◽  
Linear Models ◽  
Effective Connectivity ◽  
Causal Modeling ◽  
Detection Methods ◽  
Information Theoretic ◽  
Model Based ◽  
Model Free ◽  
Directed Information ◽  
The Brain

Effective connectivity refers to the influence one neural system exerts on another and corresponds to the parameter of a model that tries to explain the observed dependencies. In this sense, effective connectivity corresponds to the intuitive notion of coupling or directed causal influence. Traditional measures to quantify the effective connectivity include model-based methods, such as dynamic causal modeling (DCM), Granger causality (GC), and information-theoretic methods. Directed information (DI) has been a recently proposed information-theoretic measure that captures the causality between two time series. Compared to traditional causality detection methods based on linear models, directed information is a model-free measure and can detect both linear and nonlinear causality relationships. However, the effectiveness of using DI for capturing the causality in different models and neurophysiological data has not been thoroughly illustrated to date. In addition, the advantage of DI compared to model-based measures, especially those used to implement Granger causality, has not been fully investigated. In this paper, we address these issues by evaluating the performance of directed information on both simulated data sets and electroencephalogram (EEG) data to illustrate its effectiveness for quantifying the effective connectivity in the brain.

scholarly journals Awakening: Predicting external stimulation to force transitions between different brain states

2019 ◽  
Vol 116 (36) ◽  
pp. 18088-18097 ◽  
Author(s):  
Gustavo Deco ◽  
Josephine Cruzat ◽  
Joana Cabral ◽  
Enzo Tagliazucchi ◽  
Helmut Laufs ◽  
...  
Keyword(s):  
Fundamental Problem ◽  
Effective Connectivity ◽  
Brain State ◽  
Whole Brain ◽  
External Stimulation ◽  
Neuropsychiatric Diseases ◽  
Brain Modeling ◽  
Brain States ◽  
Definition Of ◽  
The Brain

A fundamental problem in systems neuroscience is how to force a transition from one brain state to another by external driven stimulation in, for example, wakefulness, sleep, coma, or neuropsychiatric diseases. This requires a quantitative and robust definition of a brain state, which has so far proven elusive. Here, we provide such a definition, which, together with whole-brain modeling, permits the systematic study in silico of how simulated brain stimulation can force transitions between different brain states in humans. Specifically, we use a unique neuroimaging dataset of human sleep to systematically investigate where to stimulate the brain to force an awakening of the human sleeping brain and vice versa. We show where this is possible using a definition of a brain state as an ensemble of “metastable substates,” each with a probabilistic stability and occurrence frequency fitted by a generative whole-brain model, fine-tuned on the basis of the effective connectivity. Given the biophysical limitations of direct electrical stimulation (DES) of microcircuits, this opens exciting possibilities for discovering stimulation targets and selecting connectivity patterns that can ensure propagation of DES-induced neural excitation, potentially making it possible to create awakenings from complex cases of brain injury.

scholarly journals Network analysis of whole-brain fMRI dynamics: A new framework based on dynamic communicability

2018 ◽  
Author(s):  
Matthieu Gilson ◽  
Nikos E. Kouvaris ◽  
Gustavo Deco ◽  
Jean-François Mangin ◽  
Cyril Poupon ◽  
...  
Keyword(s):  
Network Analysis ◽  
Network Theory ◽  
Random Network ◽  
Effective Connectivity ◽  
Structural Connectivity ◽  
Real Data ◽  
Brain Regions ◽  
Network Activity ◽  
Whole Brain ◽  
The Brain

AbstractNeuroimaging techniques such as MRI have been widely used to explore the associations between brain areas. Structural connectivity (SC) captures the anatomical pathways across the brain and functional connectivity (FC) measures the correlation between the activity of brain regions. These connectivity measures have been much studied using network theory in order to uncover the distributed organization of brain structures, in particular FC for task-specific brain communication. However, the application of network theory to study FC matrices is often “static” despite the dynamic nature of time series obtained from fMRI. The present study aims to overcome this limitation by introducing a network-oriented analysis applied to whole-brain effective connectivity (EC) useful to interpret the brain dynamics. Technically, we tune a multivariate Ornstein-Uhlenbeck (MOU) process to reproduce the statistics of the whole-brain resting-state fMRI signals, which provides estimates for MOU-EC as well as input properties (similar to local excitabilities). The network analysis is then based on the Green function (or network impulse response) that describes the interactions between nodes across time for the estimated dynamics. This model-based approach provides time-dependent graph-like descriptor, named communicability, that characterize the roles that either nodes or connections play in the propagation of activity within the network. They can be used at both global and local levels, and also enables the comparison of estimates from real data with surrogates (e.g. random network or ring lattice). In contrast to classical graph approaches to study SC or FC, our framework stresses the importance of taking the temporal aspect of fMRI signals into account. Our results show a merging of functional communities over time (in which input properties play a role), moving from segregated to global integration of the network activity. Our formalism sets a solid ground for the analysis and interpretation of fMRI data, including task-evoked activity.

scholarly journals Effective connectivity inferred from fMRI transition dynamics during movie viewing points to a balanced reconfiguration of cortical interactions

2017 ◽  
Author(s):  
Matthieu Gilson ◽  
Gustavo Deco ◽  
Karl Friston ◽  
Patric Hagmann ◽  
Dante Mantini ◽  
...  
Keyword(s):  
Effective Connectivity ◽  
Functional Integration ◽  
Brain Regions ◽  
Connectivity Pattern ◽  
Model Parameters ◽  
Auditory Information ◽  
Brain Areas ◽  
Brain Hemispheres ◽  
Dynamic Coordination ◽  
The Brain

AbstractOur behavior entails a flexible and context-sensitive interplay between brain areas to integrate information according to goal-directed requirements. How-ever, the neural mechanisms governing the entrainment of functionally specialized brain areas remain poorly understood. In particular, the question arises whether observed changes in the regional activity for different cognitive conditions are explained by modifications of the inputs to the brain or its connectivity? We observe that transitions of fMRI activity between areas convey information about the tasks performed by 19 subjects, watching a movie versus a black screen (rest). We use a model-based framework that explains this spatiotemporal functional connectivity pattern by the local variability for 66 cortical regions and the network effective connectivity between them. We find that, among the estimated model parameters, movie viewing affects to a larger extent the local activity, which we interpret as extrinsic changes related to the increased stimulus load. However, detailed changes in the effective connectivity preserve a balance in the propagating activity and select specific pathways such that high-level brain regions integrate visual and auditory information, in particular boosting the communication between the two brain hemispheres. These findings speak to a dynamic coordination underlying the functional integration in the brain.

scholarly journals Extracting orthogonal subject- and behavior-specific signatures from fMRI data using whole-brain effective connectivity

2017 ◽  
Author(s):  
Vicente Pallarés ◽  
Andrea Insabato ◽  
Ana Sanjuán ◽  
Simone Kühn ◽  
Dante Mantini ◽  
...  
Keyword(s):  
Effective Connectivity ◽  
Real Life ◽  
Brain Regions ◽  
Fmri Data ◽  
Whole Brain ◽  
Related Information ◽  
Subject Identification ◽  
The Subject ◽  
And Behavior ◽  
Robust Prediction

AbstractThe study of brain communication based on fMRI data is often limited because such measurements are a mixture of session-to-session variability with subject- and condition-related information. Disentangling these contributions is crucial for real-life applications, in particular when only a few recording sessions are available. The present study aims to define a reliable standard for the extraction of multiple signatures from fMRI data, while verifying that they do not mix information about the different modalities. In particular, condition-specific signature should not be contaminated by subject-related information. Practically, signatures correspond to subnetworks of directed interactions between brain regions (typically 100 covering the whole brain) supporting the subject and condition identification for single fMRI sessions. The key for robust prediction is using effective connectivity instead of functional connectivity. Our method demonstrates excellent generalization capabilities for subject identification in two datasets, using only a few sessions per subject as reference. Using another dataset with resting state and movie viewing, we show that the two signatures related to subjects and tasks correspond to distinct subnetworks, which are thus topologically orthogonal. Our results set solid foundations for applications tailored to individual subjects, such as clinical diagnostic.

scholarly journals A Mixed-Modeling Framework for Whole-Brain Dynamic Network Analysis

2021 ◽  
Author(s):  
Mohsen Bahrami ◽  
Paul J. Laurienti ◽  
Heather M. Shappell ◽  
Sean L. Simpson
Keyword(s):  
Network Analysis ◽  
Brain Networks ◽  
System Level ◽  
Phenotypic Traits ◽  
Modeling Framework ◽  
Whole Brain ◽  
Model Based ◽  
Dynamic Patterns ◽  
The Relationship ◽  
The Brain

AbstractThe emerging area of dynamic brain network analysis has gained considerable attraction in recent years. While current tools have proven useful in providing insight into dynamic patterns of brain networks, development of multivariate statistical frameworks that allow for examining the associations between phenotypic traits and dynamic patterns of system-level properties of the brain, and drawing statistical inference about such associations, has largely lagged behind. To address this need we developed a mixed-modeling framework that allows for assessing the relationship between any desired phenotype and dynamic patterns of whole-brain connectivity and topology. Unlike current tools which largely use data-driven methods, our model-based method enables aligning neuroscientific hypotheses with the analytic approach. We demonstrate the utility of this model in identifying the relationship between fluid intelligence and dynamic brain networks using resting-state fMRI (rfMRI) data from 200 subjects in the Human Connectome Project (HCP) study. To our knowledge, this approach provides the first model-based statistical method for examining dynamic patterns of system-level properties of the brain and their relationships to phenotypic traits.

scholarly journals Extracting orthogonal subject- and condition-specific signatures from fMRI data using whole-brain effective connectivity

NeuroImage ◽  
2018 ◽  
Vol 178 ◽  
pp. 238-254 ◽  
Author(s):  
Vicente Pallarés ◽  
Andrea Insabato ◽  
Ana Sanjuán ◽  
Simone Kühn ◽  
Dante Mantini ◽  
...  
Keyword(s):  
Effective Connectivity ◽  
Fmri Data ◽  
Whole Brain
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