Multivariate autoregressive modeling of fMRI time series
| Autor: | Lee M. Harrison, Karl J. Friston, William D. Penny |
|---|---|
| Rok vydání: | 2003 |
| Předmět: |
Multivariate statistics
Computer science Cognitive Neuroscience Bayesian probability Prefrontal Cortex Inference Fixation Ocular Machine learning computer.software_genre Synaptic Transmission Parietal Lobe Humans Attention Mathematical Computing Cerebral Cortex Brain Mapping Motivation Series (mathematics) Functional integration (neurobiology) Estimation theory business.industry Linear model Magnetic Resonance Imaging Nonlinear system Nonlinear Dynamics Pattern Recognition Visual Neurology Autoregressive model Multivariate Analysis Linear Models Regression Analysis Neural Networks Computer Artificial intelligence Nerve Net Arousal business Algorithm computer Algorithms |
| Zdroj: | NeuroImage. 19:1477-1491 |
| ISSN: | 1053-8119 |
| DOI: | 10.1016/s1053-8119(03)00160-5 |
| Popis: | We propose the use of multivariate autoregressive (MAR) models of functional magnetic resonance imaging time series to make inferences about functional integration within the human brain. The method is demonstrated with synthetic and real data showing how such models are able to characterize interregional dependence. We extend linear MAR models to accommodate nonlinear interactions to model top-down modulatory processes with bilinear terms. MAR models are time series models and thereby model temporal order within measured brain activity. A further benefit of the MAR approach is that connectivity maps may contain loops, yet exact inference can proceed within a linear framework. Model order selection and parameter estimation are implemented by using Bayesian methods. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full Text from ScienceDirect