Generalised filtering and stochastic DCM for fMRI

Li, Baojuan; Daunizeau, Jean; Stephan, Klaas Ε.; Penny, Will D.; Hu, Dewen; Friston, Karl J.

doi:10.1016/j.neuroimage.2011.01.085

Cited by 187 publications

(208 citation statements)

References 37 publications

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“…We chose to use this dataset because it has been extensively studied with other methods of connectivity, including structural equation modeling (SEM; Büchel and Friston, 1997;Penny et al, 2004b), autoregressive models , and different variants of DCM for fMRI (Friston et al, , 2014aLi et al, 2011;Marreiros et al, 2008;Penny et al, 2004b;Stephan et al, 2008). Here, sparse rDCM was used to invert a fully connected model (Fig.…”

Section: Empirical Data: Small Network "Attention To Motion" Datasetmentioning

confidence: 99%

“…First, we restricted our analyses to small networks to evaluate the utility of the approach for models that are of typical size for conventional DCM analyses. We used two previously published fMRI datasets: First, the "attention-to-motion" dataset, which has been employed to introduce various methodological developments, including structural equation models (SEM; Büchel and Friston, 1997;Penny et al, 2004b) and several variants of DCM for fMRI (Friston et al, , 2014aLi et al, 2011;Marreiros et al, 2008;Penny et al, 2004b;Stephan et al, 2008). Second, a dataset of stroke patients with aphasia, which has been used for model-based classification by generative embedding (Brodersen et al, 2011).…”

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A generative model of whole-brain effective connectivity

et al. 2018

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A B S T R A C TThe development of whole-brain models that can infer effective (directed) connection strengths from fMRI data represents a central challenge for computational neuroimaging. A recently introduced generative model of fMRI data, regression dynamic causal modeling (rDCM), moves towards this goal as it scales gracefully to very large networks. However, large-scale networks with thousands of connections are difficult to interpret; additionally, one typically lacks information (data points per free parameter) for precise estimation of all model parameters.This paper introduces sparsity constraints to the variational Bayesian framework of rDCM as a solution to these problems in the domain of task-based fMRI. This sparse rDCM approach enables highly efficient effective connectivity analyses in whole-brain networks and does not require a priori assumptions about the network's connectivity structure but prunes fully (all-to-all) connected networks as part of model inversion. Following the derivation of the variational Bayesian update equations for sparse rDCM, we use both simulated and empirical data to assess the face validity of the model. In particular, we show that it is feasible to infer effective connection strengths from fMRI data using a network with more than 100 regions and 10,000 connections. This demonstrates the feasibility of whole-brain inference on effective connectivity from fMRI data -in single subjects and with a run-time below 1 min when using parallelized code. We anticipate that sparse rDCM may find useful application in connectomics and clinical neuromodeling -for example, for phenotyping individual patients in terms of wholebrain network structure. IntroductionThe human brain comprises multiple levels of organization, with cognitive functions arising from the interplay of functional specialization and integration (Sporns, 2013;Tononi et al., 1994). With the advent of non-invasive neuroimaging techniques, such as functional magnetic resonance imaging (fMRI), researchers have begun to systematically study these fundamental properties of human brain organization. While early neuroimaging studies focused on localizing cognitive processes to specific brain regions, contemporary studies are commonly concerned with functional integration of these regions; this requires analyzing brain connectivity (Smith, 2012). In addition to analyses of structural (anatomical) connections, assessments of functional connectivity (statistical dependencies between network nodes) play a major role. Particularly, functional connectivity has been used frequently for studying the functional organization of large (whole-brain) networks both in tasks and in the "resting state" (i.e., unconstrained cognition in the absence of external perturbations). Various methods have been proposed (for a comprehensive review, see Karahanoglu and Van De Ville, 2017), ranging from conventional correlation or coherence analyses which assume stationarity (Fox et al., 2005) to sliding-window correlation analyses that can capture dyna...

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Section: Empirical Data: Small Network "Attention To Motion" Datasetmentioning

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A generative model of whole-brain effective connectivity

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“…We used a non-liner stochastic DCM approach (28,79) to estimate the functional coupling within a four-region network in which each region was linked to a separate task or decision component (Table S3). The primary purpose of this DCM analysis was to examine associations between interregional functional connectivity and behavior (i.e., responsibility aversion and leadership).…”

Section: Dynamic Causal Modeling (Dcm) Network Analysismentioning

confidence: 99%

Computational and neurobiological foundations of leadership decisions

Edelson

Polanía

Ruff

et al. 2018

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Decisions as diverse as committing soldiers to the battlefield or picking a school for your child share a basic attribute: assuming responsibility for the outcome of others. This responsibility is inherent in the roles of prime ministers and generals, as well as in the more quotidian roles of firmmanagers, schoolteachers, and parents. Here we identify the underlying behavioral, computational, and neurobiologicalmechanisms that determine the choice to assume responsibility over others. Leaders must take responsibility for others and affect the well-being of individuals, organizations, and nations. We identify the effects of responsibility on leaders' choices at the behavioral and neurobiological level and document the widespread existence of responsibility aversion, i.e., a reduced willingness to make decisions if the welfare of others is at stake. In mechanistic terms, basic preferences towards risk, losses and ambiguity do not explain responsibility aversion which, instead, is driven by a second-order cognitive process reflecting an increased demand for certainty about the best choice when others' welfare is affected. Finally, models estimating levels of information flow between brain regions processing separate choice components, provide the first step in understanding the neurobiological basis of individual variability in responsibility aversion and leadership scores. We identify and characterize the computations and neural mechanisms underlying choices to lead. This file includes:Hyperlinked Table of Contents Materials and Methods Supplementary ResultsFigs. S1 to S8 Tables S1 to S7 References and notes Appendix S1Appendix S22 Participants and sample size determination.We conducted the experiment with two separate samples of participants -marked throughout the manuscript as original and fMRI replication groups. The difference between the groups was that the latter performed the delegation task in the MRI scanner. Previous laboratory experiments on individual versus group decision making have typically used between 30-50 participants (37-39).Power calculations (40) based on the aforementioned studies average effect sizes suggested a stopping criterion of 40 participants as a reasonable estimate to ensure a statistical power of 0.8 (with an alpha level of 0.05). We thus recruited 40 participants for the original group (21 females; age 25.7 ± 0.66 standard error of the mean). In the fMRI replication group, we added, a priori, four additional participants (constituting one unit of participants, see below, resulting in 44 participants; 25 females; age 23.5 ± 0.43). This was done in anticipation of some minor data loss due to issues such as excessive head movement in the scanner, and because the minimum experimental session size could not be under eight participants (see task design below). The data for three participants were not fully collected (two participants failed the test quiz assessing comprehension of the instructions and one participant did not show up for the second stage), resulting in a fi...

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“…The state equation of stochastic DCMs accounts for random fluctuations at the neuronal level (Li et al 2011):…”

Section: The Evolution Of Dcm For Fmri: Nonlinear Two-state Stochasmentioning

confidence: 99%