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A strategy of model space search for dynamic causal modeling in task fMRI data exploratory analysis

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Abstract

Dynamic causal modeling (DCM) is a tool used for effective connectivity (EC) estimation in neuroimage analysis. But it is a model-driven analysis method, and the structure of the EC network needs to be determined in advance based on a large amount of prior knowledge. This characteristic makes it difficult to apply DCM to the exploratory brain network analysis. The exploratory analysis of DCM can be realized from two perspectives: one is to reduce the computational cost of the model; the other is to reduce the model space. From the perspective of model space reduction, a model space exploration strategy is proposed, including two algorithms. One algorithm, named GreedyEC, starts with reducing EC from full model, and the other, named GreedyROI, start with adding EC from one node model. Then the two algorithms were applied to the task state functional magnetic resonance imaging (fMRI) data of visual object recognition and selected the best DCM model from the perspective of model comparison based on Bayesian model compare method. Results show that combining the results of the two algorithms can further improve the effect of DCM exploratory analysis. For convenience in application, the algorithms were encapsulated into MATLAB function based on SPM to help neuroscience researchers to analyze the brain causal information flow network. The strategy provides a model space exploration tool that may obtain the best model from the perspective of model comparison and lower the threshold of DCM analysis.

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Data availability

The data that support the findings of this study are openly available in OpenNeuro at https://openneuro.org/datasets/ds000105, reference number ds000105.

Abbreviations

DCM:

Dynamic causal modeling

FC:

Functional connectivity

EC:

Effective connectivity

GCA:

Granger causality analysis

rDCM:

Regression dynamic causal modeling

srDCM:

Sparse regression DCM

SNR:

Signal-to-noise ratio

TR:

Repetition time

GES:

Greedy Equivalence Search

GHD:

Greedy Hamming-Distance Search

GA:

Genetic Algorithm

BMA:

Bayesian model average

ROI:

Region of interest

BMS:

Bayesian model selection

EPI:

Echo planar imaging

SPGR:

Spoiled gradient recall

MNI:

Montreal Neurological Institute

V1:

Primary visual cortex area

FG:

Fusiform gyrus

IPS:

Intraparietal sulcus

IT:

Inferior temporal lobe

AAL:

Anatomical automatic labeling

GBF:

Group Bayes Factor

BPA:

Bayesian parameters average

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Acknowledgements

We wish to thank Dr. Haxby JV for providing public datasets.

Funding

This work was supported by the National Key Research and Development Program of China (Grant No. 2018AAA0102100), the Natural Science Foundation of Hunan Province of China (Grant Nos. 2019JJ40387 and 2020JJ4120), the National Natural Science Foundation of China (Grant Nos. 61972419 and 61672542), the Hunan Philosophy and Social Science Foundation Project (Grant No. 20JD039), and also supported by the Fundamental Reasearch Funds for the Central Universities of Central South University (Hosted by Yilin Ou).

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Authors and Affiliations

  1. School of Computer Science and Engineering, Central South University, Changsha, 410083, China

    Yilin Ou, Peishan Dai, Xiaoyan Zhou, Tong Xiong, Yang Li, Zailiang Chen & Beiji Zou

  2. Hunan Engineering Research Center of Machine Vision and Intelligent Medicine, Central South University, Changsha, 410083, China

    Peishan Dai, Yang Li, Zailiang Chen & Beiji Zou

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  1. Yilin Ou
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  2. Peishan Dai
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  3. Xiaoyan Zhou
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Contributions

Project design: YO and PD. Project conception: PD, ZC and BZ. Conducting of experiments: YO, PD, XZ, TX and YL. Analysis of data: YO and PD. Interpretation of data: YO, PD, XZ, TX and YL. Manuscript writing: YO, PD and XZ. Manuscript editing: YO, PD and ZC. Manuscript review: PD and BZ.

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Correspondence to Peishan Dai.

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Ou, Y., Dai, P., Zhou, X. et al. A strategy of model space search for dynamic causal modeling in task fMRI data exploratory analysis. Phys Eng Sci Med 45, 867–882 (2022). https://doi.org/10.1007/s13246-022-01156-w

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