Simultaneous Brain, Brainstem and Spinal Cord pharmacological-fMRI reveals endogenous opioid network interactions mediating attentional analgesia

Oliva,; Hartley-Davies, Ron; Moran, Rosalyn J.; Pickering, Anthony E.; Jc, Brooks

doi:10.1101/2021.05.05.442823

Cited by 2 publications

(5 citation statements)

References 74 publications

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“…Finally, we demonstrated the validity of our EPI-based automation approach in an independently acquired large-scale cortico-spinal dataset (N > 100; Oliva et al, 2021). In this case the automated approach exceeded the performance of manual selection (though that we were not able to test this performance advantage in a further independent data-set).…”

Section: Discussionmentioning

confidence: 85%

“…In order to validate the EPI-based automation method (which performed at least as well as the manual approach), we obtained an independent, externally acquired data set of spinal fMRI data. 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, 2021). Here, we report results based on analyzing the z-shim reference data from 117 acquisitions (39 participants, each with three visits).…”

Section: Validation Of Epi-based Automation Approachmentioning

confidence: 99%

“…In order to validate the EPI-based automation approach we obtained an externally acquired corticospinal fMRI dataset consisting of 113 EPI z-shim reference scans acquired on a different MR-system (Oliva et al 2021), 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 thatcompared 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%).…”

Section: Validation Of Epi-based Automation Approachmentioning

confidence: 99%

“…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, 2021) in order to validate our candidate approach for automating the selection of slice-specific z-shims.…”

Section: Introductionmentioning

confidence: 99%

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

Kaptan¹,

Vannesjo

Mildner³

et al. 2021

Preprint

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Functional magnetic resonance imaging (fMRI) of the human spinal cord faces many challenges, one of which is signal loss due to local magnetic field inhomogeneities. This issue can be addressed with slice-specific z-shimming, which compensates for the dephasing effect of the inhomogeneities using a single, slice-specific gradient pulse. Since the original demonstration of its utility, this technique has already been employed in several spinal fMRI studies. Here, we aim to address two outstanding issues regarding this technique: on the one hand, we evaluate its effects on several parameters that are directly relevant for spinal fMRI (but have not yet been assessed) and on the other hand, we improve upon the manual selection of slice-specific z-shims by developing automated procedures. First, we demonstrate that the beneficial effects of z-shimming i) are apparent across a large range of echo times, ii) hold true for both the dorsal and ventral horn gray matter, and iii) are also clearly apparent in the temporal signal-to-noise ratio (tSNR) of gradient-echo EPI time-series data. Second, and more importantly, we address the time-consuming and subjective nature of manual selection of slice-specific z-shims by developing two automated approaches: one is based on finding the z-shim that maximizes spinal cord signal intensity in each slice of an EPI z-shim reference-scan and the other is based on finding the strength of the gradient-field that compensates the through-slice inhomogeneity in field map data. Both automated approaches i) were much faster than the manual approach, ii) lead to significant improvements in spinal cord gray matter tSNR compared to no z-shimming and iii) resulted in beneficial effects that were stable across time. While the field map-based approach performed slightly worse than the manual approach, the EPI-based approach performed at least as well as the manual one and was furthermore validated on an independently acquired corticospinal data-set (N > 100). Together, we believe that automated z-shimming will improve the data quality of future spinal fMRI studies and — by removing the subjective step of manual z-shim selection — may also lead to increased reproducibility in longitudinal studies.

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Section: Discussionmentioning

confidence: 85%

Section: Validation Of Epi-based Automation Approachmentioning

confidence: 99%

Section: Validation Of Epi-based Automation Approachmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Kaptan¹,

Vannesjo

Mildner³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Each of these co-exist within the descending pain modulatory system (DPMS) and depend on partially overlapping signalling systems and circuits. For example, placebo analgesia in part requires descending opioidergic pathways ( 4 ), conditioned pain modulation is mediated by mainly noradrenergic and partly opioidergic signalling ( 5 , 6 ), whereas attentional analgesia, stress-induced analgesia/hyperalgesia and offset analgesia are associated with the periaqueductal grey-dorsal raphe axis ( 7 – 9 ).…”

Section: Introductionmentioning

confidence: 99%