Reliability of Dynamic Causal Modeling using the Statistical Parametric Mapping Toolbox
نویسندگان
چکیده
منابع مشابه
Reliability of Dynamic Causal Modeling using the Statistical Parametric Mapping Toolbox
Dynamic causal modeling (DCM) is a recently developed approach for effective connectivity measurement in the brain. It has attracted considerable attention in recent years and quite widespread used to investigate brain connectivity in response to different tasks as well as auditory, visual, and somatosensory stimulation. This method uses complex algorithms, and currently the only implementation...
متن کاملmpdcm: A toolbox for massively parallel dynamic causal modeling.
BACKGROUND Dynamic causal modeling (DCM) for fMRI is an established method for Bayesian system identification and inference on effective brain connectivity. DCM relies on a biophysical model that links hidden neuronal activity to measurable BOLD signals. Currently, biophysical simulations from DCM constitute a serious computational hindrance. Here, we present Massively Parallel Dynamic Causal M...
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Dynamic causal modeling (DCM) is a Bayesian framework for inferring effective connectivity among brain regions from neuroimaging data. While the validity of DCM has been investigated in various previous studies, the reliability of DCM parameter estimates across sessions has been examined less systematically. Here, we report results of a software comparison with regard to test-retest reliability...
متن کاملStatistical Parametric Mapping
1. INTRODUCTION This chapter is about making regionally specific inferences in neuroimaging. These inferences may be about differences expressed when comparing one group of subjects to another or, within subjects, over a sequence of observations. They may pertain to structural differences (e.g. in voxel-based morphometry-Ashburner and Friston 2000) or neurophysiological indices of brain functio...
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ژورنال
عنوان ژورنال: International Journal of System Dynamics Applications
سال: 2014
ISSN: 2160-9772,2160-9799
DOI: 10.4018/ijsda.2014040101