Temporal Grading Index of Functional Network Topology Predicts Pain Perception of Patients With Chronic Back Pain

Figures

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  Figure 1. The pipeline of TGI extraction. (A) We assess the dynamic functional connectivity (dFC) using the sliding window analysis with 50 TRs length for a window and 1 TR length for sliding step, resulting in t windows. (B) We extract dynamic property matrix (degree, clustering coefficient [CC], and participation coefficient [PC]) for the network of each CBP patient and the average network of the HCs. A linear regression model was then used to extract the TGI Ki, j of each network property of the j-th brain region for i-th CBP subject.

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  Figure 2. Distribution of the TGI of different network properties [degree, clustering coefficient (CC), and participation coefficient (PC)] in cerebrum and cerebellum.

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  Figure 3. Prediction performance of pain intensity using the TGI of different network properties. The scatter plot shows the correlation between the real VAS score and the predicted VAS score estimated by different features through different regression models. The solid lines indicate the identity line (y = x). CC, clustering coefficient; PC, participation coefficient; VAS, visual analog scale.

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  Figure 4. The MSE is derived from different models and features. Bars represent the mean and SD of the MSE during the cross-validation process. CC, clustering coefficient; PC, participation coefficient.

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  Figure 5. Brain regions have high prediction power and the fluctuation of their nodal topology over time. (A) The weight of nodes contributed to the prediction process. Larger nodal size indicates a higher weight. (B) The fluctuation of network properties [nodal degree, clustering coefficient (CC), and participation coefficient (PC), not TGI] of the five nodes with the highest weights. The black point indicates a significant between-group difference (p < 0.05, FDR corrected). vCa, ventral caudate; A44op, opercular Broca's area (opercular Brodmann area 44); V, cerebellum lobule V; A20rv, rostroventral inferior temporal gyrus (rostroventral Brodmann area 20); dIa, dorsal agranular insula; VIIIa, cerebellum lobule VIIIa; dId, dorsal dysgranular insula; msOccG, medial superior occipital gyrus; cpSTS, caudoposterior superior temporal sulcus; IFJ, inferior frontal junction; rTtha, rostral temporal thalamus; CrusI, cerebellum lobule Crus I; IX, cerebellum lobule IX.