Fig. 1: Overview of analytic pipeline and group assignments.

a Study Work Flow: Data processing and analytic workflow: BOLD brain oxygen level dependent, MRS magnetic resonance spectroscopy, HCA hierarchical cluster analysis, VOI volume-of-interest, CRP plasma C-reactive protein, ReHo regional homogeneity, ROI region-of-interest. b Dendrogram/heatmap of hierarchical clustering analysis (HCA): Using HCA, the sample was divided into High (n = 22) and Low (n = 19) plasma C-reactive protein (CRP)-glutamate (Glu) groups. The accompanying tree-shaped dendrogram and Heatmap illustrate the clustering algorithm. In HCA, the data is arranged hierarchically with log-normalized (Ln) CRP as first node of the hierarchy followed by Ln Left Basal (Glu) as the secondary node. The agglomerative nature of the method used where minor clusters are progressively combined to yield larger clusters is also depicted as branches of the dendrogram. The length of dendrogram lines are proportional to cluster distances. The Legends along the side of the dendrogram provide the range of individual subject measures of the variables used (Ln CRP and Ln LB Glu) color-coded as cool (green)-warm (red) colors. c CRP and Glu distribution: The scatterplot demonstrates the distribution of the association between CRP and LB Glu in the High vs. Low CRP-Glu groups. Shaded 80% confidence-interval ellipses are used to represent High (red) and Low (green) CRP-Glu groups (respectively). Values of CRP and LB Glu in x and y-axis (respectively), were quantile-normalized (norm quant) for easy visual inspection and color-coded for the two groups of interest. The broken lines represent the median values. Using median-split 2/3rd of subjects were classified as having both high CRP/high glutamate in contrast to 1/3rd of subjects with high glutamate who did not fall into the high CRP group (Chi Sq = 4.7, p = 0.03). Thus, the association between CRP and glutamate was neither absolute or invariable with neither qualifying to be a proxy for the other