Simultaneous brain, brainstem, and spinal cord pharmacological-fMRI reveals involvement of an endogenous opioid network in attentional analgesia

Abstract

Pain perception is decreased by shifting attentional focus away from a threatening event. This attentional analgesia engages parallel descending control pathways from anterior cingulate (ACC) to locus coeruleus, and ACC to periaqueductal grey (PAG) - rostral ventromedial medulla (RVM), indicating possible roles for noradrenergic or opioidergic neuromodulators. To determine which pathway modulates nociceptive activity in humans we used simultaneous whole brain-spinal cord pharmacological-fMRI (N=39) across three sessions. Noxious thermal forearm stimulation generated somatotopic-activation of dorsal horn (DH) whose activity correlated with pain report and mirrored attentional pain modulation. Activity in an adjacent cluster reported the interaction between task and noxious stimulus. Effective connectivity analysis revealed that ACC interacts with PAG and RVM to modulate spinal cord activity. Blocking endogenous opioids with Naltrexone impairs attentional analgesia and disrupts RVM-spinal and ACC-PAG connectivity. Noradrenergic augmentation with Reboxetine did not alter attentional analgesia. Cognitive pain modulation involves opioidergic ACC-PAG-RVM descending control which suppresses spinal nociceptive activity.

Automated slice-specific z-shimming for fMRI of the human spinal cord

“…In order to validate the EPI-based automation approach, we obtained an externally acquired corticospinal GE-EPI dataset consisting of 113 EPI z-shim reference scans acquired on a different MR-system (Oliva et al 2022), which also allowed us to investigate the generalizability of the EPI-based automated approach in a dataset in which the manual selection was conducted by a different researcher (VO). Using this independently acquired data set, we observed that – compared to no z-shim – manual z-shimming resulted in a significant increase in mean signal intensity (t (112) = 19.24, p < .001, difference of 22.1%, CI: 19.7-24.4%) and a significant decrease in signal intensity variation across slices (t (112) = 8.83, p < .001, difference of 37.1%, CI: 29.7- 43.9%).…”

“…These data were acquired by VO, RHD, and JCWB as part of a larger project on pharmacological aspects of cortico-spinal pain modulation (Oliva et al, 2022). Here, we report results based on analyzing the z-shim reference data from 117 acquisitions (39 participants, each with three visits).…”

“…While this type of acquisition is used by numerous groups when studying somatomotor (e.g. Maieron et al, 2007;Vahdat et al, 2015;Weber et al, 2016;Kinany et al, 2019), somatosensory (Brooks et al 2012;Tinnermann et al, 2017;Weber et al, 2020;Oliva et al, 2022) or resting-state spinal cord responses (Kong et al, 2014;San Emeterio Nateras, 2016;Kinany et al, 2020), there is also a strong tradition of using spin-echo approaches (for reviews, see e.g. Stroman, 2005 andPowers et al, 2018) and a more recent development in using multi-shot acquisitions (e.g.…”

“…Sprenger et al, 2012; Geuter & Buchel, 2013; Kong et al, 2014; van de Sand et al, 2015; Eippert et al, 2017; Sprenger et al, 2018) and cortico-spinal fMRI studies (e.g. Sprenger et al, 2015; Tinnermann et al, 2017; Vahdat et al, 2020; Oliva et al, 2022), the impact of slice-specific z-shimming on EPI time-series data has not been investigated systematically, as Finsterbusch and colleagues only evaluated its effects on single volumes of GE EPI data, but not on time-series metrics such as tSNR (Welvaert & Rosseel, 2013). Even more important – and already argued for by Finsterbusch and colleagues – would be an automated way to determine the slice-specific z-shims, as these are currently determined manually by the scanner operator: either visually by going through each slice and z-shim value obtained in a reference-scan or by manually placing a region of interest on each slice of this reference scan and evaluating the extracted signal intensity.…”

“…The second method is based on a field map acquisition and determines z-shim values by estimating the strength of the gradient field needed to compensate for the local through-slice inhomogeneity for each slice. In a final step, we use an independently-acquired external data-set (N>100; Oliva et al, 2022) in order to validate our candidate approach for automating the selection of slice-specific z-shims. Automated z-shimming for spinal fMRI 6…”