Intrinsic organization of cortical networks predicts state anxiety: an functional near-infrared spectroscopy (fNIRS) study

Figures

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  Fig. 1. Schematic representations of optodes and channels. A The configuration of the fNIRS probes and B the localization of the fNIRS measurement channels.

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  Fig. 2. Schematic of the model fit. To fit the predictive model, we used a stratified eightfold cross-validation approach. We performed eight iterations of model training and testing. To determine the regularization parameter alpha, the training set of each iteration was entered into a nested leave-one-out cross-validation process. Specifically, in iteration k (k = 1, 2,⋯, 8), we traversed the alpha values (α) from 0.01 to 10 in steps of 0.01. For each α, we conducted a leave-one-out cross-validation and calculated the corresponding mean squared error, MSE(α). Then we picked the α which minimized MSE(α), denoted as αk. We averaged αk (k = 1, 2,⋯, 8) and obtained a mean value α*, which was used as the regularization parameter for model training.

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  Fig. 3. Distribution of anxiety scores and performance of the prediction model. A Frequency distribution of levels of state anxiety. B Frequency distribution of the MSE scores for the prediction analysis in state anxiety. C Scatter plot for the relationship between self-report scores and predicted scores in state anxiety. D Frequency distribution of levels of trait anxiety. E Frequency distribution of the MSE scores for the prediction analysis in trait anxiety. F Scatter plot for the relationship between self-report scores and predicted scores in trait anxiety. G Scatter plot for the relationship between state and trait anxiety.

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  Fig. 4. Contributing RSFC in the prediction of state anxiety. A The definition of nodes in the default mode network (DMN) and frontal-parietal network (FPN). B Brain regions showed negatively weighted connectivity in the DMN and FPN. C The definition of nodes in the DMN, FPN, dorsal attention network (DAN), and salience network (SN). D Brain regions showed positively weighted connectivity in the DMN, FPN, DAN, and SN.

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  Fig. 5. Connectivity patterns of the six contributing networks. Patterns of A both negatively and positively weighted connectivity. B Connectivity within the DMN, between the DMN and DAN, negatively predicted levels of state anxiety. C Connectivity of the FPN with the DMN, DAN, and SN positively contributed to the prediction of state anxiety. See Fig. 1 for the location for each number of the optode. DMN default mode network, FPN frontoparietal network, DAN dorsal attention network, SN salience network, SMN somatomotor network, LN limbic network.