scholarly journals Effective connectivity during animacy perception – dynamic causal modelling of Human Connectome Project data

2014 ◽  
Vol 4 (1) ◽  
Author(s):  
Hauke Hillebrandt ◽  
Karl J. Friston ◽  
Sarah-Jayne Blakemore
Keyword(s):  
Effective Connectivity ◽  
Causal Modelling ◽  
Human Connectome Project ◽  
Project Data ◽  
Dynamic Causal Modelling

scholarly journals The neurophysiological architecture of semantic dementia: spectral dynamic causal modelling of a neurodegenerative proteinopathy

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Elia Benhamou ◽  
Charles R. Marshall ◽  
Lucy L. Russell ◽  
Chris J. D. Hardy ◽  
Rebecca L. Bond ◽  
...  
Keyword(s):  
Network Architecture ◽  
Large Scale ◽  
Effective Connectivity ◽  
Brain Regions ◽  
Semantic Dementia ◽  
Older Individuals ◽  
World Knowledge ◽  
Causal Modelling ◽  
Dynamic Causal Modelling ◽  
Semantic Impairment

Abstract The selective destruction of large-scale brain networks by pathogenic protein spread is a ubiquitous theme in neurodegenerative disease. Characterising the circuit architecture of these diseases could illuminate both their pathophysiology and the computational architecture of the cognitive processes they target. However, this is challenging using standard neuroimaging techniques. Here we addressed this issue using a novel technique—spectral dynamic causal modelling—that estimates the effective connectivity between brain regions from resting-state fMRI data. We studied patients with semantic dementia—the paradigmatic disorder of the brain system mediating world knowledge—relative to healthy older individuals. We assessed how the effective connectivity of the semantic appraisal network targeted by this disease was modulated by pathogenic protein deposition and by two key phenotypic factors, semantic impairment and behavioural disinhibition. The presence of pathogenic protein in SD weakened the normal inhibitory self-coupling of network hubs in both antero-mesial temporal lobes, with development of an abnormal excitatory fronto-temporal projection in the left cerebral hemisphere. Semantic impairment and social disinhibition were linked to a similar but more extensive profile of abnormally attenuated inhibitory self-coupling within temporal lobe regions and excitatory projections between temporal and inferior frontal regions. Our findings demonstrate that population-level dynamic causal modelling can disclose a core pathophysiological feature of proteinopathic network architecture—attenuation of inhibitory connectivity—and the key elements of distributed neuronal processing that underwrite semantic memory.

Poster #T190 EFFECTIVE CONNECTIVITY IN SCHIZOPHRENIA – DYNAMIC CAUSAL MODELLING OF THE MISMATCH NEGATIVITY

2014 ◽  
Vol 153 ◽  
pp. S356
Author(s):  
Siri M. Ranlund ◽  
Alvaro Diez ◽  
Rick A. Adams ◽  
Harriet Brown ◽  
Muriel Walshe ◽  
...  
Keyword(s):  
Mismatch Negativity ◽  
Effective Connectivity ◽  
Causal Modelling ◽  
Dynamic Causal Modelling

scholarly journals Dynamic causal modelling of fluctuating connectivity in resting-state EEG

2018 ◽  
Author(s):  
Frederik Van de Steen ◽  
Hannes Almgren ◽  
Adeel Razi ◽  
Karl Friston ◽  
Daniele Marinazzo
Keyword(s):  
Effective Connectivity ◽  
Basis Functions ◽  
State Transitions ◽  
Common Component ◽  
Default Mode ◽  
Causal Modelling ◽  
Subject Specific ◽  
Temporal Basis ◽  
Dynamic Connectivity ◽  
Dynamic Causal Modelling

ABSTRACTFunctional and effective connectivity are known to change systematically over time. These changes might be explained by several factors, including intrinsic fluctuations in activity-dependent neuronal coupling and contextual factors, like experimental condition and time. Furthermore, contextual effects may be subject-specific or conserved over subjects. To characterize fluctuations in effective connectivity, we used dynamic causal modelling (DCM) of cross spectral responses over 1 min of electroencephalogram (EEG) recordings during rest, divided into 1-sec windows. We focused on two intrinsic networks: the default mode and the saliency network. DCM was applied to estimate connectivity in each time-window for both networks. Fluctuations in DCM connectivity parameters were assessed using hierarchical parametric empirical Bayes (PEB). Within-subject, between-window effects were modelled with a second-level linear model with temporal basis functions as regressors. This procedure was conducted for every subject separately. Bayesian model reduction was then used to assess which (combination of) temporal basis functions best explain dynamic connectivity over windows. A third (between-subject) level model was used to infer which dynamic connectivity parameters are conserved over subjects. Our results indicate that connectivity fluctuations in the saliency network comprised both subject-specific components and a common component. For the default mode network, connectivity trajectories only showed a common component. For both networks, connections to higher order regions appear to monotonically increase during the one minute period. These results not only establish the predictive validity of dynamic connectivity estimates – in virtue of detecting systematic changes over subjects – they also suggest a network-specific dissociation in the relative contribution of fluctuations in connectivity that depend upon experimental context. We envisage these procedures could be useful for characterizing brain state transitions that may be explained by their cognitive or neuropathological underpinnings.

scholarly journals Dynamic causal modelling of effective connectivity during perspective taking in a communicative task

NeuroImage ◽  
2013 ◽  
Vol 76 ◽  
pp. 116-124 ◽  
Author(s):  
Hauke Hillebrandt ◽  
Iroise Dumontheil ◽  
Sarah-Jayne Blakemore ◽  
Jonathan P. Roiser
Keyword(s):  
Perspective Taking ◽  
Effective Connectivity ◽  
Causal Modelling ◽  
Dynamic Causal Modelling

scholarly journals Dynamic Causal Modelling of the Reduced Habituation to Painful Stimuli in Migraine: An EEG Study

2020 ◽  
Vol 10 (10) ◽  
pp. 712
Author(s):  
Iege Bassez ◽  
Frederik Van de Steen ◽  
Katia Ricci ◽  
Eleonora Vecchio ◽  
Eleonora Gentile ◽  
...  
Keyword(s):  
Effective Connectivity ◽  
Anterior Cingulate ◽  
Group Differences ◽  
Sensory Stimuli ◽  
Consistent Finding ◽  
Causal Modelling ◽  
Migraine Group ◽  
Somatosensory Areas ◽  
Dynamic Causal Modelling

A consistent finding in migraine is reduced cortical habituation to repetitive sensory stimuli. This study investigated brain dynamics underlying the atypical habituation to painful stimuli in interictal migraine. We investigated modulations in effective connectivity between the sources of laser evoked potentials (LEPs) from a first to final block of trigeminal LEPs using dynamic causal modelling (DCM) in a group of 23 migraine patients and 20 controls. Additionally, we looked whether the strength of dynamical connections in the migrainous brain is initially different. The examined network consisted of the secondary somatosensory areas (lS2, rS2), insulae (lIns, rIns), anterior cingulate cortex (ACC), contralateral primary somatosensory cortex (lS1), and a hidden source assumed to represent the thalamus. Results suggest that migraine patients show initially heightened communication between lS1 and the thalamus, in both directions. After repetitive stimulations, connection strengths from the thalamus to all somatosensory areas habituated in controls whereas this was not apparent in migraine. Together with further abnormalities in initial connectivity strengths and modulations between the thalamus and the insulae, these results are in line with altered thalamo-cortical network dynamics in migraine. Group differences in connectivity from and to the insulae including interhemispheric connections, suggests an important role of the insulae.

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