Bayesian Modelling of fMRI lime Series
نویسندگان
چکیده
We present a Hidden Markov Model (HMM) for inferring the hidden psychological state (or neural activity) during single trial fMRI activation experiments with blocked task paradigms. Inference is based on Bayesian methodology, using a combination of analytical and a variety of Markov Chain Monte Carlo (MCMC) sampling techniques. The advantage of this method is that detection of short time learning effects between repeated trials is possible since inference is based only on single trial experiments.
منابع مشابه
Time series forecasting of Bitcoin price based on ARIMA and machine learning approaches
Bitcoin as the current leader in cryptocurrencies is a new asset class receiving significant attention in the financial and investment community and presents an interesting time series prediction problem. In this paper, some forecasting models based on classical like ARIMA and machine learning approaches including Kriging, Artificial Neural Network (ANN), Bayesian method, Support Vector Machine...
متن کاملMultivariate Autoregressive Modelling of fMRI time series
We propose the use of Multivariate Autoregressive (MAR) models of fMRI 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 characterise inter-regional dependence. We extend linear MAR models to accommodate nonlinear interactions to model top-down modulatory processes with ...
متن کاملNetwork discovery with DCM
This paper is about inferring or discovering the functional architecture of distributed systems using Dynamic Causal Modelling (DCM). We describe a scheme that recovers the (dynamic) Bayesian dependency graph (connections in a network) using observed network activity. This network discovery uses Bayesian model selection to identify the sparsity structure (absence of edges or connections) in a g...
متن کاملBayesian Inference for Functional Dynamics Exploring in fMRI Data
This paper aims to review state-of-the-art Bayesian-inference-based methods applied to functional magnetic resonance imaging (fMRI) data. Particularly, we focus on one specific long-standing challenge in the computational modeling of fMRI datasets: how to effectively explore typical functional interactions from fMRI time series and the corresponding boundaries of temporal segments. Bayesian inf...
متن کاملCluster priors in the Bayesian modelling of fMRI data
Taskinen, Ilkka Cluster priors in the Bayesian modelling of fMRI data Jyväskylä: University of Jyväskylä, 2001, 105 p. ISBN 951-39-1059-8 ISSN 1457-8905 Diss. Functional magnetic resonance imaging (fMRI) is a scanning technique for revealing haemodynamic changes connected with brain processing on the neuronal level. In neuropsychology, fMRI has been used in designed experiments together with co...
متن کامل