Sensitivity of functional connectivity to periaqueductal gray localization, with implications for identifying disease-related changes in chronic visceral pain: A MAPP Research Network neuroimaging study

Figures

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  Fig. 1. Analysis of the BOLD rs-fMRI time series signal from the three different localization techniques of the PAG. A. Top left: An 3D illustration of the brain highlighting the brainstem. Top right: The average normalized rs-fMRI across participants in the healthy control dataset. Bottom: The three different regions of interest (ROIs) for the PAG: a previously published Montreal Neurological Institute (MNI) set of coordinates surrounded by a 3 mm radius sphere (MNI-sphere in green, shown top right in average image), a hand-traced ROI in a MNI template brain (MNI-trace in blue, shown top right in average image), and a hand-drawn ROI in each of the structural images from healthy control participants (participant-trace in red). B. Example rs-fMRI time series signals comparing the MNI-sphere ROI and the MNI-trace ROI to the participant-trace ROI. C. The differences in connectivity across all healthy controls (test dataset, n = 15, p < 0.001 and validation dataset, n = 15, p < 0.0001). The connectivity is given by the standardized correlation of the signal extracted from the MNI-sphere ROI to the participant-trace ROI and from the MNI-trace ROI to the participant-trace ROI. (For…

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  Fig. 2. Whole-brain functional connectivity analysis of three different localization techniques of the PAG. A. A measure of functional connectivity from each PAG ROI to 264 regions throughout the brain defined by the Power 264 atlas. An example of rs-fMRI BOLD residual time series signal from an atlas region and its connectivity measure to each PAG ROI. The connectivity error of the example region and the MNI-sphere ROI to the participant-trace ROI (z2-z1) and the connectivity error of the example region and the MNI-trace ROI to the participant-trace ROI (z3-z1) are calculated per healthy control participant. B. The distribution of the connectivity error as calculated in A (n = 15, test dataset). For this example region, smaller differences are seen in the MNI-trace ROI to the participant-trace ROI as compared to the MNI-sphere ROI to the participant-trace ROI. Signals from A come from the participant circled in purple. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

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  Fig. 3. Performance of the PAG ROIs on whole-brain functional connectivity. A. Brain regions whose interquartile range (IQR) of the connectivity error was smaller and median closer to zero (D) in the MNI-trace ROI to participant-trace ROI compared to the MNI-sphere ROI to participant-trace ROI. B. Power 264 atlas regions whose interquartile range of the connectivity error were greater and median further to zero in their connectivity to the MNI-trace ROI to participant-trace ROI compared to the MNI-sphere ROI to participant-trace ROI. The comparison was performed at a for each Power 264 atlas region (A.i and B.i) and at each voxel within a brain-masked covering the entire brain (A.ii and B.ii). The analysis included the test dataset (n = 15), validation dataset (n = 15), and overlap of the two datasets. Key: SMM, somatosensory/motor (hand), SMMm, somatosensory/motor (mouth), COTC, cingulo-opercular task-control, Aud, auditory, DMN, default-mode, MR, memory retrieval, Vis, visual, FPTC, frontoparietal task-control, SN, salience, subC, subcortical, VAN, ventral attention, DAN, dorsal attention, CBL, cerebellum, Other, other regions.

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  Fig. 4. Functional connectivity differences in UPPS patients versus healthy controls. A. Selection of clusters based on significant voxelwise connectivity in healthy controls to the MNI-sphere ROI (HC: p < 0.00001, α=0.01 , uc = 6). B. Selection of clusters based on significant voxelwise connectivity in healthy controls to the MNI-trace ROI HC: p < 0.00001, α=0.01, uc = 6). Uncorrected significant clusters identified between patients and healthy controls are circled in purple (HC vs. UCPPS: p < 0.05) and corrected significant clusters are identified with an asterisk (HC vs. UCPPS: p < 0.01, FDR q < 0.05). Key: HC, healthy control, UCPPS, patients. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

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  Fig. 5. Independent component analysis (ICA) correction for potential physiological noise effects without ICA application and across FIX thresholds 5, 40 and 75. A. The differences in connectivity across healthy controls in test (n = 15) and validation (n = 15) datasets (No ICA: p < 0.001, FIX Thr.5: p < 0.001, FIX Thr.40: p < 0.0001, FIX Thr.75: p < 0.0001) B. A comparison of UCPPS patient versus healthy control connectivity differences to the MNI-trace ROI in significant clusters, left inferior frontal gyrus (IF) and left inferior parietal lobule (IP), from Fig. 4 (IF HC vs. UCPPS: No ICA: p < 0.01, FIX Thr.5: p < 0.01, IP HC vs. UCPPS: p < 0.01, FIX Thr.5: p < 0.01, IF HC No ICA vs IF HC FIX Thr.75: p < 0.01, IF HC FIX Thr.5 vs IF HC FIX Thr.75: p < 0.001). C. ICA noise component contribution at the MNI-trace ROI, IF, and IP, calculated from the maximum value at each location and averaged across participants. Threshold values are based on the number of participants with a signal above a voxel threshold of 0.1. Key: HC, healthy control, UCPPS, patients, IF, left inferior frontal gyrus, IP, left inferior parietal lobule.