A guide to group effective connectivity analysis, part 1: First level analysis with DCM for fMRI

Zeidman, Peter; Jafarian, Amirhossein; Corbin, Nadège; Seghier, Mohamed L.; Razi, Adeel; Price, Cathy J.; Friston, Karl J.

doi:10.1016/j.neuroimage.2019.06.031

Cited by 270 publications

(344 citation statements)

References 37 publications

(67 reference statements)

Supporting

Mentioning

310

Contrasting

Order By: Relevance

“…However, unlike previous studies, we did not find a modulation of backward connections, which may be due to a number of reasons. First, we have taken advantage of the recently developed parametric empirical Bayes (PEB) approach (Friston et al, 2016;Zeidman et al, 2019) , which allows more precise inferences at the single parameter level, informed by empirical priors taken from group-level estimates, as compared to classical random-fixed effect modelling. Second, the stimulation used in past studies is inherently different from ours.…”

Section: Discussionmentioning

confidence: 99%

“…We then used parametric empirical Bayes (PEB) (Friston et al, 2016;Zeidman et al, 2019) had been re-evaluated using the group means as priors to obtain the most robust estimates (Zeidman et al, 2019) . An advantage of PEB, as opposed to classical random-effects (RFX) analysis, is that PEB takes not only the mean, but also the uncertainty of individual connection strengths into account.…”

Section: Parametric Empirical Bayesian (Peb) Analysis Of Group Effectsmentioning

confidence: 99%

See 1 more Smart Citation

Learning of complex auditory patterns changes intrinsic and feedforward effective connectivity between Heschl’s gyrus and planum temporale

Lumaca

Dietz

Quiroga-Martinez

et al. 2019

Preprint

View full text Add to dashboard Cite

Learning of complex auditory sequences such as language and music can be thought of as the continuous optimisation of internal predictive representations of sound-pattern regularities, driven by prediction errors. In predictive coding (PC), this occurs through changes in the intrinsic and extrinsic connectivity of the relevant cortical networks, whereby minimization of precision-weighted prediction error signals improves the accuracy of future predictions. Here, we employed Dynamic Causal Modelling (DCM) on functional magnetic resonance (fMRI) data acquired during the presentation of complex auditory patterns. In an oddball paradigm, we presented 52 volunteers (non-musicians) with isochronous 5-tone melodic patterns (standards), randomly interleaved with rare novel patterns comprising contour or pitch interval changes (deviants). Here, listeners must update their standard melodic models whenever they encounter unexpected deviant stimuli. Contour deviants induced an increased BOLD response, as compared to standards, in primary (Heschl's gyrus, HG) and secondary auditory cortices (planum temporale, PT). Within this network, we found a left-lateralized increase in feedforward connectivity from HG to PT for deviant responses and a concomitant disinhibition within left HG. Consistent with PC, our results suggest that model updating in auditory pattern perception and learning is associated with specific changes in the excitatory feedforward connections encoding prediction errors and in the intrinsic connections that encode the precision of these errors and modulate their gain. Significance statementThe learning of complex auditory stimuli such as music and speech can be thought of as the continuous optimisation of brain predictive models driven by prediction errors.Using dynamic causal modelling on fMRI data acquired during a melodic oddball paradigm, we here show that brain responses to unexpected sounds were best explained by an increase in excitation within Heschl's gyrus and an increase in forward connections from Heschl's gyrus to planum temporale. Our results are consistent with a predictive coding account of sensory learning, whereby prediction error responses to new sounds drive model adjustments via feedforward connections and intrinsic connections encode the confidence of these prediction errors.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Parametric Empirical Bayesian (Peb) Analysis Of Group Effectsmentioning

confidence: 99%

Learning of complex auditory patterns changes intrinsic and feedforward effective connectivity between Heschl’s gyrus and planum temporale

Lumaca

Dietz

Quiroga-Martinez

et al. 2019

Preprint

View full text Add to dashboard Cite

show abstract

“…RPE parametric modulators were used as modulating inputs on the recursive connections between Area 9 and VS. DCMs were fitted to the data for observation and control conditions in both groups. To account for between-subject variability, DCMs were then subjected to a 3-level hierarchical analysis based on Parametric Empirical Bayes (PEB; Zeidman et al, 2019a, 2019b, Friston et al, 2016). The first level comprised a total of 8 DCM parameters (4 for the baseline connections (A-matrix) and 4 for the modulatory connections (B-matrix)) for every subject and observation condition which were subjected to PEB on the second and third levels, respectively.…”

Section: Methodsmentioning

confidence: 99%

Reward Prediction Error Signaling during Reinforcement Learning in Social Anxiety Disorder is altered by Social Observation

Becker

Voegler

Peterburs

et al. 2019

Preprint

View full text Add to dashboard Cite

BACKGROUNDDysfunctional expectations of impending social or performance outcomes are core features of Social Anxiety Disorder (SAD) but often lack formal definition in clinical research. Reinforcement learning (RL) models offer a framework to define changes in outcome expectations in a formal way by computing the prediction error (PE). This study quantifies the updating of expectations by PEs in SAD and investigates alterations in RL regions associated with PE signaling.METHOD48 adult participants (24 diagnosed with SAD and 24 age-, gender-, and education- matched healthy controls (HC)) underwent event-related functional magnetic resonance imaging while learning from probabilistic feedback. Crucially, both groups completed two parallel versions of the task: one in which they learned under scrutiny (social observation) and one in which they learned without being overtly evaluated (non-social control condition).RESULTSCoupling to prediction errors in SAD was elevated in dorsomedial prefrontal cortex (DMPFC) when learning under observation. These findings provide the first evidence that PE signaling during social performance situations in SAD is associated with hypersensitive response signatures in DMPFC, a brain region associated with using others’ value standards as a proxy for one’s own value standards. Dynamic Causal Modelling further revealed that RPE-modulated connectivity from ventral pallidum to DMPFC during observation was reduced in SAD.CONCLUSIONSThe present results corroborate a crucial role of DMPFC in SAD, which corresponds to dysfunctional expectations about others’ alleged performance standards that play a prominent role in current models of the disorder.

show abstract

“…DCM was implemented using the Statistical Parametric Mapping software (SPM12; Wellcome Department of Cognitive Neurology, London, UK) to estimate the EC between the regions of interest (ROIs; Zeidman et al, 2019). Based on the group results from a previous study (Esménio et al, 2019), we selected the brain regions associated with high-level social processing (Bzdok et al, 2012;Schurz et al, 2014;Alcalá-López et al, 2018); namely, those regions activated during both self and other condition.…”

Section: Effective Connectivity Analysismentioning

confidence: 99%

Changes in the Effective Connectivity of the Social Brain When Making Inferences About Close Others vs. the Self

Esmenio

Soares

Oliveira‐Silva

et al. 2020

Front. Hum. Neurosci.

View full text Add to dashboard Cite

Previous research showed that the ability to make inferences about our own and other's mental states rely on common brain pathways; particularly in the case of close relationships (e.g., romantic relationships). Despite the evidence for shared neural representations of self and others, less is known about the distributed processing within these common neural networks, particularly whether there are specific patterns of internode communication when focusing on other vs. self. This study aimed to characterize context-sensitive coupling among social brain regions involved in self and other understanding. Participants underwent an fMRI while watching emotional video vignettes of their romantic partner and elaborated on their partner's (other-condition) or on their own experience (self-condition). We used dynamic causal modeling (DCM) to quantify the associated changes in effective connectivity (EC) in a network of brain regions involved in social cognition including the temporoparietal junction (TPJ), the posterior cingulate (PCC)/precuneus and middle temporal gyrus (MTG). DCM revealed that: the PCC plays a central coordination role within this network, the bilateral MTG receives driving inputs from other nodes suggesting that social information is first processed in language comprehension regions; the right TPJ evidenced a selective increase in its sensitivity when focusing on the other's experience, relative to focusing on oneself.

show abstract

A guide to group effective connectivity analysis, part 1: First level analysis with DCM for fMRI

Cited by 270 publications

References 37 publications

Learning of complex auditory patterns changes intrinsic and feedforward effective connectivity between Heschl’s gyrus and planum temporale

Learning of complex auditory patterns changes intrinsic and feedforward effective connectivity between Heschl’s gyrus and planum temporale

Reward Prediction Error Signaling during Reinforcement Learning in Social Anxiety Disorder is altered by Social Observation

Changes in the Effective Connectivity of the Social Brain When Making Inferences About Close Others vs. the Self

Contact Info

Product

Resources

About