scholarly journals Calcium imaging and dynamic causal modelling reveal brain-wide changes in effective connectivity and synaptic dynamics during epileptic seizures

2018 ◽  
Vol 14 (8) ◽  
pp. e1006375 ◽  
Author(s):  
Richard E. Rosch ◽  
Paul R. Hunter ◽  
Torsten Baldeweg ◽  
Karl J. Friston ◽  
Martin P. Meyer
Keyword(s):  
Calcium Imaging ◽  
Effective Connectivity ◽  
Epileptic Seizures ◽  
Causal Modelling ◽  
Synaptic Dynamics ◽  
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.

scholarly journals Imaging and dynamic causal modelling reveal brain-wide changes in effective connectivity and synaptic dynamics during epileptic seizures

2017 ◽  
Author(s):  
RE Rosch ◽  
PR Hunter ◽  
T Baldeweg ◽  
KJ Friston ◽  
MP Meyer
Keyword(s):  
Network Architecture ◽  
Empirical Bayes ◽  
Temporal Dynamics ◽  
Spatial Scales ◽  
Field Potential ◽  
Synaptic Coupling ◽  
Zebrafish Brain ◽  
Causal Modelling ◽  
Synaptic Dynamics ◽  
Dynamic Causal Modelling

SummaryPathophysiological explanations of epilepsy typically focus on either the micro/mesoscale (e.g. excitation-inhibition imbalance), or on the macroscale (e.g. network architecture). Linking abnormalities across spatial scales remains difficult, partly because of technical limitations in measuring neuronal signatures concurrently at the scales involved. Here we use light sheet imaging of the larval zebrafish brain during acute epileptic seizure induced with pentylenetetrazole. Empirically measured spectral changes of spontaneous neuronal activity during the seizure are then modelled using neural mass models, allowing Bayesian inference on changes in effective network connectivity and their underlying synaptic dynamics. This dynamic causal modelling of seizures in the zebrafish brain reveals concurrent changes in synaptic coupling at macro- and mesoscale. Fluctuations of synaptic connection strength and their temporal dynamics are both required to explain observed seizure patterns. These findings challenge a simple excitation-inhibition account of seizures, and highlight changes in synaptic transmission dynamics as a possible seizure generation pathomechanism.AbbreviationsLFPlocal field potentialPTZpentylenetetrazoleDCMdynamic causal modellingCSDcross spectral densitiesPEBParametric Empirical Bayes

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
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