Individual differences in pain perception are of interest in basic and clinical research as altered pain sensitivity is both a characteristic and a risk factor for many pain conditions. It is, however, unclear how individual sensitivity to pain is reflected in the pain-free resting-state brain activity and functional connectivity. Here, we identify and validate a network pattern in the pain-free resting-state functional brain connectome that is predictive of interindividual differences in pain sensitivity. Our predictive network signature allows assessing the individual sensitivity to pain without applying any painful stimulation, as might be valuable in patients where reliable behavioural pain reports cannot be obtained. Additionally, as a direct, non-invasive readout of the supraspinal neural contribution to pain sensitivity, it may have implications for translational research and the development and assessment of analgesic treatment strategies.

The LDL family of receptors and its member LRP1 have classically been associated with a modulation of lipoprotein metabolism. Current studies, however, indicate diverse functions for this receptor in various aspects of cellular activities, including cell proliferation, migration, differentiation and survival. LRP1 is essential for normal neuronal function in the adult CNS, whereas the role of LRP1 in development remained unclear. Previously we have observed an upregulation of LewisX (LeX) glycosylated LRP1 in the stem cells of the developing cortex and demonstrated its importance for oligodendrocyte differentiation. In the current study we show that LeX-glycosylated LRP1 is also expressed in the stem cell compartment of the developing spinal cord and has broader functions in the developing CNS. We have investigated the basic properties of LRP1 conditional knockout on the neural stem/progenitor cells (NSPCs) from the cortex and the spinal cord, created by means of Cre-loxp mediated recombination in vitro. The functional status of LRP1-deficient cells has been studied using proliferation, differentiation and apoptosis assays. LRP1 deficient NSPCs from both CNS regions demonstrated altered differentiation profiles. Their differentiation capacity towards oligodendrocyte progenitor cells (OPCs), mature oligodendrocytes and neurons was reduced. In contrast, astrocyte differentiation was promoted. Moreover, LRP1 deletion had a negative effect on NSPCs proliferation and survival. Our observations suggest that LRP1 facilitates NSPCs differentiation via interaction with ApoE. Upon ApoE4 stimulation wild type NSPCs generated more oligodendrocytes, but LRP1 knockout cells showed no response. The effect of ApoE seems to be independent of cholesterol uptake, but is rather mediated by downstream MAPK and Akt activation.

Individual differences in pain perception are of key interest in basic and clinical research as altered pain sensitivity is both a characteristic and a risk factor for many pain conditions. It is, however, unclear how individual susceptibility to pain is reflected in the pain-free resting-state brain activity and functional connectivity. Here, we identified and validated a network pattern in the pain-free resting-state functional brain connectome that is predictive of interindividual differences in pain sensitivity. Our predictive network signature (https://spisakt.github.io/RPN-signature) allows assessing the individual susceptibility to pain without applying any painful stimulation, as might be valuable in patients where reliable behavioural pain reports cannot be obtained. Additionally, as a direct, non-invasive readout of the supraspinal neural contribution to pain sensitivity, it may have broad implications for translational research and the development and assessment of analgesic treatment strategies.

Previous studies have described the structure and function of the insular cortex in terms of spatially continuous gradients. Here we assess how spatial features of insular resting state functional organization correspond to individual pain sensitivity. From a previous multicenter study, we included 107 healthy participants, who underwent resting state functional MRI scans, T1-weighted scans and quantitative sensory testing on the left forearm. Thermal and mechanical pain thresholds were determined. Connectopic mapping, a technique using non-linear representations of functional organization was employed to describe functional connectivity gradients in both insulae. Partial coefficients of determination were calculated between trend surface model parameters summarizing spatial features of gradients, modal and modality-independent pain sensitivity. The dominant connectopy captured the previously reported posteroanterior shift in connectivity profiles. Spatial features of dominant connectopies in the right insula explained significant amounts of variance in thermal (R2 = 0.076; p < 0.001 and R2 = 0.031; p < 0.029) and composite pain sensitivity (R2 = 0.072; p < 0.002). The left insular gradient was not significantly associated with pain thresholds. Our results highlight the functional relevance of gradient-like insular organization in pain processing. Considering individual variations in insular connectopy might contribute to understanding neural mechanisms behind pain and improve objective brain-based characterization of individual pain sensitivity.

Supplemental Digital Content is Available in the Text. Patients experiencing nonspecific chronic back pain showed impaired pain-related threat and safety learning in a classical differential conditioning heat pain paradigm.

Center effects significantly limit the generalizability of brain imaging-based biomarker candidates. Although our previously published resting state functional connectivity-based predictive signature for pain sensitivity (the RPN-signature) showed remarkable out-of-center generalizability, it remained unclear which connectivity features are the most generalizable across study centers. Here, we re-trained the RPN-signature on multi-center data and found that it outperforms the single-center model in all three centers (explained variance: 26-38% vs. 16%-19%). Our results highlight that neurobiological interpretation of feature importance in predictive modelling is constrained both by center-specific artifacts and by certain characteristics (e.g. regularization) of the applied machine learning algorithm.

Pain sensitivity is known to considerably vary across individuals. While the variability in pain has been linked to structural neural correlates, it is still unclear how well these findings replicate in independent data and whether they are powerful enough to provide reliable pain sensitivity predictions on the individual level. In this study, we constructed a predictive model of pain sensitivity utilising structural MRI-based cortical thickness data from a multi-center dataset (3 centers, 131 healthy participants). Cross-validated estimates revealed a statistically significant and clinically relevant predictive performance (Pearson-r = 0.36, p < 0.0005). The predictions were found to be specific to pain sensitivity and not biased towards potential confounding effects (e.g., anxiety, stress, depression, center-effects). Analysis of model coefficients suggests that the most robust cortical thickness predictors of pain sensitivity are the right rostral anterior cingulate gyrus, left parahippocampal gyrus and left temporal pole. Cortical thickness in these regions was negatively correlated to pain sensitivity. Our results can be considered as a proof-of-concept for the capacity of brain morphology to predict pain sensitivity, paving the way towards future multimodal brain-based biomarkers of pain. Key words: predictive modelling, machine learning, gray matter, cortical thickness, pain sensitivity, rACC, parahippocampal gyrus, temporal pole, QST, pain thresholds, LASSO

Sensitivity to pain shows a remarkable interindividual variance that has been reported to both forecast and accompany various clinical pain conditions. Although pain thresholds have been reported to be associated to brain morphology, it is still unclear how well these findings replicate in independent data and whether they are powerful enough to provide reliable pain sensitivity predictions on the individual level. In this study, we constructed a predictive model of pain sensitivity (as measured with pain thresholds) using structural magnetic resonance imaging–based cortical thickness data from a multicentre data set (3 centres and 131 healthy participants). Cross-validated estimates revealed a statistically significant and clinically relevant predictive performance (Pearson r = 0.36, P < 0.0002, R 2 = 0.13). The predictions were found to be specific to physical pain thresholds and not biased towards potential confounding effects (eg, anxiety, stress, depression, centre effects, and pain self-evaluation). Analysis of model coefficients suggests that the most robust cortical thickness predictors of pain sensitivity are the right rostral anterior cingulate gyrus, left parahippocampal gyrus, and left temporal pole. Cortical thickness in these regions was negatively correlated to pain sensitivity. Our results can be considered as a proof-of-concept for the capacity of brain morphology to predict pain sensitivity, paving the way towards future multimodal brain-based biomarkers of pain.