Study of effective connectivity based on dynamic causal modeling in subtraction calculation task.
- Author:
Yan ZHANG
1
;
Chunxiao CHEN
;
Guangming LU
;
Zhiqiang ZHANG
;
Haiyan YU
;
Wei HUANG
;
Zhili CHEN
;
Yuan ZHONG
Author Information
1. Department of Medical Imaging, Nanjing General Hospital of Nanjing Military Command, Nanjing 210002, China.
- Publication Type:Journal Article
- MeSH:
Algorithms;
Bayes Theorem;
Brain;
physiology;
Female;
Humans;
Image Processing, Computer-Assisted;
Magnetic Resonance Imaging;
methods;
Male;
Mathematical Computing;
Models, Neurological;
Nerve Net;
Neural Pathways;
physiology;
Young Adult
- From:
Journal of Biomedical Engineering
2009;26(5):931-940
- CountryChina
- Language:Chinese
-
Abstract:
Dynamic causal modeling (DCM) is a spatio-temporal renewable network model. As an analytical method of causality of functional integration in fMRI, DCM is applied to study the effective connectivity. The neuro-imaging time series of activated regions were put into DCM, and the trial-bound inputs were used as perturbations to the designed model. DCM was used in combination with Bayesian estimation to evaluate the intrinsic connectivity among selected neurons. Bayes factors were used to compute different neuro-physiological models with intrinsic connectivity structures, and then were used to select the optimal model. The selected regions in this mental calculation task are the left superior parietal lobule (SPL), the left inferior parietal lobule (IPL) and the left middle frontal gyrus (MFG). Finally, the connected network in conformity to physiological significance was obtained.
