Dynamic Causal Modeling of fMRI Data


Functional imaging studies of brain damaged patients offer a unique opportunity to understand how sensorimotor and cognitive tasks can be carried out when parts of the neural system that support normal performance are no longer available. In addition to knowing which regions a patient activates, we also need to know how these regions interact with one another, and how these interregional interactions deviate from normal. Dynamic causal modeling (DCM) offers the opportunity to assess task-dependent interactions within a set of regions. Dynamical causal modelling (DCM) for functional magnetic resonance imaging (fMRI) is a technique to infer directed connectivity among brain regions. Here we review its use in patients when the question of interest concerns the characterization of abnormal connectivity for a given pathology. We describe the currently available implementations of DCM for fMRI responses, varying from the deterministic bilinear models with one-state equation to the stochastic non-linear models with two-state equations. In our proposed study BOLD-fMRI data of patients suffering from Brain Tumor, Epilepsy and Alzheimer's disease is used for DCM.

  • Abstract
  • Key Words
  • 1 Introduction
  • 2. Algorithms
  • 3. Method for Analysis of fMRI Data
  • 4. Summaries
  • References
Topics: Modeling

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