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

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.

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

NeuroImage ◽

10.1016/j.neuroimage.2019.116367 ◽

2020 ◽

Vol 208 ◽

pp. 116367 ◽

Cited By ~ 6

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

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The dynamic connectome of speech control

Philosophical Transactions of the Royal Society B Biological Sciences ◽

10.1098/rstb.2020.0256 ◽

2021 ◽

Vol 376 (1836) ◽

Cited By ~ 1

Author(s):

Davide Valeriani ◽

Kristina Simonyan

Keyword(s):

Neural Network ◽

Effective Connectivity ◽

Vocal Learning ◽

Brain Regions ◽

Brain Network ◽

Motor Output ◽

Network Activity ◽

Imaging Data ◽

Whole Brain ◽

Cost Efficient

Speech production relies on the orchestrated control of multiple brain regions. The specific, directional influences within these networks remain poorly understood. We used regression dynamic causal modelling to infer the whole-brain directed (effective) connectivity from functional magnetic resonance imaging data of 36 healthy individuals during the production of meaningful English sentences and meaningless syllables. We identified that the two dynamic connectomes have distinct architectures that are dependent on the complexity of task production. The speech was regulated by a dynamic neural network, the most influential nodes of which were centred around superior and inferior parietal areas and influenced the whole-brain network activity via long-ranging coupling with primary sensorimotor, prefrontal, temporal and insular regions. By contrast, syllable production was controlled by a more compressed, cost-efficient network structure, involving sensorimotor cortico-subcortical integration via superior parietal and cerebellar network hubs. These data demonstrate the mechanisms by which the neural network reorganizes the connectivity of its influential regions, from supporting the fundamental aspects of simple syllabic vocal motor output to multimodal information processing of speech motor output. This article is part of the theme issue ‘Vocal learning in animals and humans’.

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A distributed dopamine-gated circuit underpins reproductive state-dependent behavior in Drosophila females

10.1101/2021.07.24.453623 ◽

2021 ◽

Author(s):

Ariane C Boehm ◽

Anja B Friedrich ◽

Paul Bandow ◽

K.P. Siju ◽

Sydney Hunt ◽

...

Keyword(s):

Internal State ◽

Brain Regions ◽

Neural Pathways ◽

Reproductive State ◽

Behavioral Screening ◽

Whole Brain ◽

State Dependent ◽

Dependent Behavior ◽

Output Neurons ◽

And Behavior

Motherhood induces a drastic, sometimes long-lasting, change in internal state and behavior in most female animals. Here, we show that a mating-induced increase in olfactory attraction of female Drosophila flies to nutrients relies on interconnected neural pathways in the two higher olfactory brain regions, the lateral horn (LH) and the mushroom body (MB). Using whole brain calcium imaging, we find that mating does not induce a global change in the activity of the whole brain nor of entire brain regions, suggesting specific neuronal or network changes in the olfactory system. Systematic behavioral screening and electron microscopy (EM) connectomics identify two types of LH output neurons required for the attraction of females to polyamines -one of them previously implicated in the processing of male pheromones. In addition, we characterize multiple MB pathways capable of inducing or suppressing polyamine attraction, with synaptic connections to the identified LH neurons and a prominent role for the β′1 compartment. Moreover, β′1 dopaminergic neurons are modulated by mating and are sufficient to replace mating experience in virgins inducing the lasting behavioral switch in female preference. Taken together, our data in the fly suggests that reproductive state-dependent expression of female choice behavior is regulated by a dopamine-gated distributed learning circuit comprising both higher olfactory brain centers.

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

10.1101/223784 ◽

2017 ◽

Cited By ~ 1

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.

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Information Models for Processing Product Lifecycle Functionalities and Interfaces for Sustainable Manufacturing

Journal of Computing and Information Science in Engineering ◽

10.1115/1.4032575 ◽

2016 ◽

Vol 16 (1) ◽

Cited By ~ 2

Author(s):

Utpal Roy ◽

Mehmet I. Sarigecili

Keyword(s):

Sustainable Manufacturing ◽

Real Life ◽

Design System ◽

Sufficient Information ◽

Related Information ◽

Information Models ◽

Processing Product ◽

And Behavior ◽

Product Realization ◽

Information Framework

There is a dire need for the development of an integrated information model for organizing and managing material and manufacturing related information, required for any product realization. In this paper, such an information framework for product, process, and material modeling has been proposed to support the product's form, function and behavior, its physical and functional decompositions, its design requirements and design rationale, and its material and process needs. A detailed, real-life case study of the waterjet intensifier design system has been presented to show how this proposed framework could support, capture, and utilize all necessary information required for the selection of the intensifier's high-pressure cylinder material. The framework will also be able to provide sufficient information to quantify the ecocompatibility of products.

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

10.1101/421883 ◽

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.

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

10.1101/531830 ◽

2019 ◽

Cited By ~ 2

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.

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Individual-subject functional localization increases univariate activation but not multivariate pattern discriminability in the ‘multiple-demand’ frontoparietal network

10.1101/661934 ◽

2019 ◽

Cited By ~ 4

Author(s):

Sneha Shashidhara ◽

Floortje S. Spronkers ◽

Yaara Erez

Keyword(s):

Working Memory ◽

Spatial Organization ◽

Spatial Working Memory ◽

Verbal Working Memory ◽

Brain Regions ◽

Fmri Data ◽

Criterion Task ◽

Frontoparietal Network ◽

Subject Specific ◽

The Subject

AbstractThe frontoparietal ‘multiple-demand’ (MD) control network plays a key role in goal-directed behavior. Recent developments of multivoxel pattern analysis (MVPA) for fMRI data allow for more fine-grained investigations into the functionality and properties of brain systems. In particular, MVPA in the MD network was used to gain better understanding of control processes such as attentional effects, adaptive coding, and representation of multiple task-relevant features, but overall low decoding levels have limited its use for this network. A common practice of applying MVPA is by investigating pattern discriminability within a region-of-interest (ROI) using a template mask, thus ensuring that the same brain areas are studied in all participants. This approach offers high sensitivity, but does not take into account differences between individuals in the spatial organization of brain regions. An alternative approach uses independent localizer data for each subject to select the most responsive voxels and define individual ROIs within the boundaries of a group template. Such an approach allows for a refined and targeted localization based on the unique pattern of activity of individual subjects while ensuring that functionally similar brain regions are studied for all subjects. In the current study we tested whether using individual ROIs leads to changes in decodability of task-related neural representations as well as univariate activity across the MD network compared to when using a group template. We used three localizer tasks to separately define subject-specific ROIs: spatial working memory, verbal working memory, and a Stroop task. We then systematically assessed univariate and multivariate results in a separate rule-based criterion task. All the localizer tasks robustly recruited the MD network and evoked highly reliable activity patterns in individual subjects. Consistent with previous studies, we found a clear benefit of the subject-specific ROIs for univariate results from the criterion task, with increased activity in the individual ROIs based on the localizers’ data, compared to the activity observed when using the group template. In contrast, there was no benefit of the subject-specific ROIs for the multivariate results in the form of increased discriminability, as well as no cost of reduced discriminability. Both univariate and multivariate results were similar in the subject-specific ROIs defined by each of the three localizers. Our results provide important empirical evidence for researchers in the field of cognitive control for the use of individual ROIs in the frontoparietal network for both univariate and multivariate analysis of fMRI data, and serve as another step towards standardization and increased comparability across studies.

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Evaluating Effective Connectivity of Trust in Human–Automation Interaction: A Dynamic Causal Modeling (DCM) Study

Human Factors The Journal of the Human Factors and Ergonomics Society ◽

10.1177/0018720820987443 ◽

2021 ◽

pp. 001872082098744

Author(s):

Jiali Huang ◽

Sanghyun Choo ◽

Zachary H. Pugh ◽

Chang S. Nam

Keyword(s):

Interface Design ◽

Bayesian Model Averaging ◽

Effective Connectivity ◽

Real Life ◽

Model Averaging ◽

Brain Regions ◽

Causal Modeling ◽

Computer Algorithms ◽

Dynamic Causal Modeling ◽

Lateral Connection

Objective Using dynamic causal modeling (DCM), we examined how credibility and reliability affected the way brain regions exert causal influence over each other—effective connectivity (EC)—in the context of trust in automation. Background Multiple brain regions of the central executive network (CEN) and default mode network (DMN) have been implicated in trust judgment. However, the neural correlates of trust judgment are still relatively unexplored in terms of the directed information flow between brain regions. Method Sixteen participants observed the performance of four computer algorithms, which differed in credibility and reliability, of the system monitoring subtask of the Air Force Multi-Attribute Task Battery (AF-MATB). Using six brain regions of the CEN and DMN commonly identified to be activated in human trust, a total of 30 (forward, backward, and lateral) connection models were developed. Bayesian model averaging (BMA) was used to quantify the connectivity strength among the brain regions. Results Relative to the high trust condition, low trust showed unique presence of specific connections, greater connectivity strengths from the prefrontal cortex, and greater network complexity. High trust condition showed no backward connections. Conclusion Results indicated that trust and distrust can be two distinctive neural processes in human–automation interaction—distrust being a more complex network than trust, possibly due to the increased cognitive load. Application The causal architecture of distributed brain regions inferred using DCM can help not only in the design of a balanced human–automation interface design but also in the proper use of automation in real-life situations.

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Individual-subject Functional Localization Increases Univariate Activation but Not Multivariate Pattern Discriminability in the “Multiple-demand” Frontoparietal Network

Journal of Cognitive Neuroscience ◽

10.1162/jocn_a_01554 ◽

2020 ◽

Vol 32 (7) ◽

pp. 1348-1368 ◽

Cited By ~ 2

Author(s):

Sneha Shashidhara ◽

Floortje S. Spronkers ◽

Yaara Erez

Keyword(s):

Working Memory ◽

Spatial Organization ◽

Spatial Working Memory ◽

Verbal Working Memory ◽

Brain Regions ◽

Fmri Data ◽

Criterion Task ◽

Frontoparietal Network ◽

Subject Specific ◽

The Subject

The frontoparietal “multiple-demand” (MD) control network plays a key role in goal-directed behavior. Recent developments of multivoxel pattern analysis (MVPA) for fMRI data allow for more fine-grained investigations into the functionality and properties of brain systems. In particular, MVPA in the MD network was used to gain better understanding of control processes such as attentional effects, adaptive coding, and representation of multiple task-relevant features, but overall low decoding levels have limited its use for this network. A common practice of applying MVPA is by investigating pattern discriminability within a ROI using a template mask, thus ensuring that the same brain areas are studied in all participants. This approach offers high sensitivity but does not take into account differences between individuals in the spatial organization of brain regions. An alternative approach uses independent localizer data for each subject to select the most responsive voxels and define individual ROIs within the boundaries of a group template. Such an approach allows for a refined and targeted localization based on the unique pattern of activity of individual subjects while ensuring that functionally similar brain regions are studied for all subjects. In the current study, we tested whether using individual ROIs leads to changes in decodability of task-related neural representations as well as univariate activity across the MD network compared with when using a group template. We used three localizer tasks to separately define subject-specific ROIs: spatial working memory, verbal working memory, and a Stroop task. We then systematically assessed univariate and multivariate results in a separate rule-based criterion task. All the localizer tasks robustly recruited the MD network and evoked highly reliable activity patterns in individual subjects. Consistent with previous studies, we found a clear benefit of the subject-specific ROIs for univariate results from the criterion task, with increased activity in the individual ROIs based on the localizers' data, compared with the activity observed when using the group template. In contrast, there was no benefit of the subject-specific ROIs for the multivariate results in the form of increased discriminability, as well as no cost of reduced discriminability. Both univariate and multivariate results were similar in the subject-specific ROIs defined by each of the three localizers. Our results provide important empirical evidence for researchers in the field of cognitive control for the use of individual ROIs in the frontoparietal network for both univariate and multivariate analysis of fMRI data and serve as another step toward standardization and increased comparability across studies.

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