Abstract
As the concept of integrating global neuron coupling effect is increasingly accepted, investigating causal connection increasingly requires the intervention of large-scale analysis. In this study, a large-scale brain network analysis was carried out by a description length guided framework, which involves a unified Granger causality analysis (uGCA) method and now integrates the concept of large-scale analysis. This will be helpful to make a more comprehensive determination for causal connection among the global brain regions. Distinct from the conventional GCA, which involves a two-stage scheme consisting of Akaike information criterion or Bayesian information criterion (AIC/BIC) and F -test to obtain a causal effect, a unified guided framework can ensure more reliable results while eliminating some confounding influences among network nodes. Then, we performed large-scale network simulation experiments involving 13 nodes; it was found that our proposal was more accurate and robust in guiding the causal connection investigation of large-scale networks. When it comes to the resting-state fMRI datasets, we studied a 90-node network selected from the Anatomical Automatic Labeling (AAL) template. Then, combining a K-means clustering method, we found that most brain nodes in the connection network obtained by uGCA methods were gathered into the corresponding functional brain regions and functionally related regions cooperated with each other. Compared to conventional GCA, their results were more consistent with clinical and anatomical priors. Moreover, in studies of several large-scale functional networks involving default mode network (DMN), dorsal attention network (DAN), and frontoparietal control network (FCN), the uGCA method more clearly revealed their empirical cooperation. As a brain with numerous nodes and massive connections, a unified large-scale analysis method is of great significance for the integration of causal connections in the whole brain network in the future.
Highlights
With the rise of a notion that the brain works as a union of complex neural circuits at different spatial scales, more information should be taken into account in describing brain region couplings; more attention should be paid to investigating causal connection among brain regions
To alter the conventional Granger causality analysis (GCA) framework, we proposed a unified model selection approach for GCA based on the minimum description length (MDL) principle, called unified Granger causality analysis (uGCA), and we had demonstrated its effectiveness and priority over the conventional GCA in our previous studies [12, 13]
With the help of the MDL principle, which provides a generic solution for the model selection issue [14–17] and regards the probability distribution as a descriptive standpoint to choose the model with the shortest description of data, we propose a unified description length guided GCA method, namely, uGCA
Summary
With the rise of a notion that the brain works as a union of complex neural circuits (functional integration) at different spatial scales, more information should be taken into account in describing brain region couplings; more attention should be paid to investigating causal connection among brain regions. Compared to conventional GCA, uGCA unifies these two generalized model selection issues into a description length guided framework It can integrate all candidate data into the same framework, so the established data model will be under the same context and can be used for large-scale network analysis more directly and effectively. This unified methodological framework is consistent with existing scientific theories and experiments, which will bring some advantages for future experimental research on mutiscale. The corresponding explanations are presented and we demonstrated the comparison between conventional two-stage GCA and our proposal
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