Dynamic Causal Modelling of Brain Responses

2009 ◽  
pp. 237-261 ◽  
Author(s):  
Karl J. Friston
Keyword(s):  
Causal Modelling ◽  
Brain Responses ◽  
Dynamic Causal Modelling

scholarly journals Dynamic Causal Modelling and physiological confounds: A functional MRI study of vagus nerve stimulation

NeuroImage ◽  
2010 ◽  
Vol 52 (4) ◽  
pp. 1456-1464 ◽  
Author(s):  
Sébastien Reyt ◽  
Chloé Picq ◽  
Valérie Sinniger ◽  
Didier Clarençon ◽  
Bruno Bonaz ◽  
...  
Keyword(s):  
Vagus Nerve ◽  
Functional Mri ◽  
Nerve Stimulation ◽  
Vagus Nerve Stimulation ◽  
Causal Modelling ◽  
Mri Study ◽  
Dynamic Causal Modelling

FMRI Dynamic Causal Modelling with Inferred Regions of Interest

NeuroImage ◽  
2009 ◽  
Vol 47 ◽  
pp. S168 ◽  
Author(s):  
M Woolrich ◽  
T Behrens ◽  
S Jbabdi
Keyword(s):  
Regions Of Interest ◽  
Causal Modelling ◽  
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 Structure learning in coupled dynamical systems and dynamic causal modelling

2019 ◽  
Vol 377 (2160) ◽  
pp. 20190048 ◽  
Author(s):  
Amirhossein Jafarian ◽  
Peter Zeidman ◽  
Vladimir Litvak ◽  
Karl Friston
Keyword(s):  
Dynamical Systems ◽  
Network Architecture ◽  
Bayesian Model ◽  
Structure Learning ◽  
Dynamical Interaction ◽  
Causal Modelling ◽  
Ill Posed ◽  
Competing Models ◽  
Neuronal Systems ◽  
Dynamic Causal Modelling

Identifying a coupled dynamical system out of many plausible candidates, each of which could serve as the underlying generator of some observed measurements, is a profoundly ill-posed problem that commonly arises when modelling real-world phenomena. In this review, we detail a set of statistical procedures for inferring the structure of nonlinear coupled dynamical systems (structure learning), which has proved useful in neuroscience research. A key focus here is the comparison of competing models of network architectures—and implicit coupling functions—in terms of their Bayesian model evidence. These methods are collectively referred to as dynamic causal modelling. We focus on a relatively new approach that is proving remarkably useful, namely Bayesian model reduction, which enables rapid evaluation and comparison of models that differ in their network architecture. We illustrate the usefulness of these techniques through modelling neurovascular coupling (cellular pathways linking neuronal and vascular systems), whose function is an active focus of research in neurobiology and the imaging of coupled neuronal systems. This article is part of the theme issue ‘Coupling functions: dynamical interaction mechanisms in the physical, biological and social sciences'.

scholarly journals Tracking slow modulations in synaptic gain using dynamic causal modelling: Validation in epilepsy

NeuroImage ◽  
2015 ◽  
Vol 107 ◽  
pp. 117-126 ◽  
Author(s):  
Margarita Papadopoulou ◽  
Marco Leite ◽  
Pieter van Mierlo ◽  
Kristl Vonck ◽  
Louis Lemieux ◽  
...  
Keyword(s):  
Causal Modelling ◽  
Synaptic Gain ◽  
Dynamic Causal Modelling

scholarly journals Prediction, precision, and context: dynamic causal modelling of MEG and ECoG

2016 ◽  
Vol 108 ◽  
pp. 19-20
Author(s):  
Ryszard Auksztulewicz ◽  
Karl Friston ◽  
Anna Ch. Nobre
Keyword(s):  
Causal Modelling ◽  
Dynamic Causal Modelling
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