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 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 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 Analysis of fMRI data using noise-diffusion network models: a new covariance-coding perspective

2017 ◽  
Author(s):  
Matthieu Gilson
Keyword(s):  
Resting State ◽  
Effective Connectivity ◽  
Network Models ◽  
Resting State Fmri ◽  
Brain Regions ◽  
Brain Network ◽  
Fmri Data ◽  
Second Order Statistics ◽  
Proposed Model ◽  
The Many

AbstractSince the middle of the 1990s, studies of resting-state fMRI/BOLD data have explored the correlation patterns of activity across the whole brain, which is referred to as functional connectivity (FC). Among the many methods that have been developed to interpret FC, a recently proposed model-based approach describes the propagation of fluctuating BOLD activity within the recurrently connected brain network by inferring the effective connectivity (EC). In this model, EC quantifies the strengths of directional interactions between brain regions, viewed from the proxy of BOLD activity. In addition, the tuning procedure for the model provides estimates for the local variability (input variances) to explain how the observed FC is generated. Generalizing, the network dynamics can be studied in the context of an input-output mapping - determined by EC - for the second-order statistics of fluctuating nodal activities. The present paper focuses on the following detection paradigm: observing output covariances, how discriminative is the (estimated) network model with respect to various input covariance patterns? An application with the model fitted to experimental fMRI data - movie viewing versus resting state - illustrates that changes in excitability and changes in brain coordination go hand in hand.

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 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

scholarly journals Visual Information Shapes the Dynamics of Corticobasal Ganglia Pathways during Response Selection and Inhibition

2015 ◽  
Vol 27 (7) ◽  
pp. 1344-1359 ◽  
Author(s):  
Sara Jahfari ◽  
Lourens Waldorp ◽  
K. Richard Ridderinkhof ◽  
H. Steven Scholte
Keyword(s):  
Visual Cortex ◽  
Visual Information ◽  
Response Selection ◽  
Selection Process ◽  
Effective Connectivity ◽  
Action Selection ◽  
Visual Input ◽  
Fmri Data ◽  
Response Strategies ◽  
Fast Flow

Action selection often requires the transformation of visual information into motor plans. Preventing premature responses may entail the suppression of visual input and/or of prepared muscle activity. This study examined how the quality of visual information affects frontobasal ganglia (BG) routes associated with response selection and inhibition. Human fMRI data were collected from a stop task with visually degraded or intact face stimuli. During go trials, degraded spatial frequency information reduced the speed of information accumulation and response cautiousness. Effective connectivity analysis of the fMRI data showed action selection to emerge through the classic direct and indirect BG pathways, with inputs deriving form both prefrontal and visual regions. When stimuli were degraded, visual and prefrontal regions processing the stimulus information increased connectivity strengths toward BG, whereas regions evaluating visual scene content or response strategies reduced connectivity toward BG. Response inhibition during stop trials recruited the indirect and hyperdirect BG pathways, with input from visual and prefrontal regions. Importantly, when stimuli were nondegraded and processed fast, the optimal stop model contained additional connections from prefrontal to visual cortex. Individual differences analysis revealed that stronger prefrontal-to-visual connectivity covaried with faster inhibition times. Therefore, prefrontal-to-visual cortex connections appear to suppress the fast flow of visual input for the go task, such that the inhibition process can finish before the selection process. These results indicate response selection and inhibition within the BG to emerge through the interplay of top–down adjustments from prefrontal and bottom–up input from sensory cortex.

scholarly journals Cognitive Factories: Modeling Situated Entropy in Physical Work Carried Out by Humans and Robots

Entropy ◽  
2018 ◽  
Vol 20 (9) ◽  
pp. 659 ◽  
Author(s):  
Stephen Fox ◽  
Adrian Kotelba ◽  
Ilkka Niskanen
Keyword(s):  
Conceptual Framework ◽  
Distributed Cognition ◽  
New Technologies ◽  
Information Gain ◽  
Human Cognition ◽  
Physical Work ◽  
Theoretical Frameworks ◽  
Assembly Work ◽  
Factory Production ◽  
Physical Components

Entropy in factories is situated. For example, there can be numerous different ways of picking, orientating, and placing physical components during assembly work. Physical components can be redesigned to increase the Information Gain they provide and so reduce situated entropy in assembly work. Also, situated entropy is affected by the extent of knowledge of those doing the work. For example, work can be done by knowledgeable experts or by beginners who lack knowledge about physical components, etc. The number of different ways that work can be done and the knowledge of the worker combine to affect cognitive load. Thus, situated entropy in factories relates to situated cognition within which knowledge is bound to physical contexts and knowing is inseparable from doing. In this paper, six contributions are provided for modelling situated entropy in factories. First, theoretical frameworks are brought together to provide a conceptual framework for modelling. Second, the conceptual framework is related to physical production using practical examples. Third, Information Theory mathematics is applied to the examples and a preliminary methodology in presented for modelling in practice. Fourth, physical artefacts in factory production are reframed as carriers of Information Gain and situated entropy, which may or may not combine as Net Information Gain. Fifth, situated entropy is related to different types of cognitive factories that involve different levels of uncertainty in production operations. Sixth, the need to measure Net Information Gain in the introduction of new technologies for embodied and extended cognition is discussed in relation to a taxonomy for distributed cognition situated in factory production. Overall, modelling of situated entropy is introduced as an opportunity for improving the planning and control of factories that deploy human cognition and cognitive technologies including assembly robotics.

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