A brain-based pain facilitation mechanism contributes to painful diabetic polyneuropathy.

The descending pain modulatory system represents one of the oldest and most fundamentally important neurophysiological mechanisms relevant to pain. Extensive work in animals and humans has shown how a functional imbalance between the facilitatory and inhibitory components is linked to exacerbation and maintenance of persistent pain states. Forward translation of these findings into clinical populations is needed to verify the relevance of this imbalance. Diabetic polyneuropathy is one of the most common causes of chronic neuropathic pain; however, the reason why ∼25–30% of patients with diabetes develop pain is not known. The current study used a multimodal clinical neuroimaging approach to interrogate whether the sensory phenotype of painful diabetic polyneuropathy involves altered function of the ventrolateral periaqueductal grey—a key node of the descending pain modulatory system. We found that ventrolateral periaqueductal grey functional connectivity is altered in patients suffering from painful diabetic polyneuropathy; the magnitude of which is correlated to their spontaneous and allodynic pain as well as the magnitude of the cortical response elicited by an experimental tonic heat paradigm. We posit that ventrolateral periaqueductal grey-mediated descending pain modulatory system dysfunction may reflect a brain-based pain facilitation mechanism contributing to painful diabetic polyneuropathy.Funding for this work was generously provided from the following sources: National Institute for Health Research Oxford Biomedical Research Centre, Medical Research Council of Great Britain and Northern Ireland, the Wellcome Trust (London, UK) and the Innovative Medicines Initiative Joint Undertaking (Brussels, Belgium), under grant agreement no 115007 resources of which are composed of financial contribution from the European Union’s Seventh Framework Programme (FP7/2007–2013) and EFPIA companies’ in kind contribution. D.L.B. and A.C.T. are members of the DOLORisk consortium funded by the European Commission Horizon 2020 (ID633491). D.L.B. and A.C.T. are members of the International Diabetic Neuropathy Consortium, the Novo Nordisk Foundation (Ref. NNF14SA0006). D.L.B. is a senior Wellcome clinical scientist (Ref. 202747/Z/16/Z). The project was supported by a strategic award from the Wellcome (Ref. 102645). A.R.S., D.L.B., and I.T. are members of the Wellcome Pain Consortium (Ref. 102645). A.C.T. is an Honorary Research Fellow of the Brain Function Research Group, School of Physiology, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa

Calibration of arterial spin labeling data—potential pitfalls in post‐processing

PurposeTo assess the impact of the different post‐processing options in the calibration of arterial spin labeling (ASL) data on perfusion quantification and its reproducibility.Theory and MethodsAbsolute quantification of perfusion measurements is one of the promises of ASL techniques. However, it is highly dependent on a calibration procedure that involves a complex processing pipeline for which no standardized procedure has been fully established. In this work, we systematically compare the main ASL calibration methods as well as various post‐processing calibration options, using 2 data sets acquired with the most common sequences, pulsed ASL and pseudo‐continuous ASL.ResultsSignificant and sometimes large discrepancies in ASL perfusion quantification were obtained when using different post‐processing calibration options. Nevertheless, when using a set of theoretically based and carefully chosen options, only small differences were observed for both reference tissue and voxelwise methods. The voxelwise and white matter reference tissue methods were less sensitive to post‐processing options than the cerebrospinal fluid reference tissue method. However, white matter reference tissue calibration also produced poorer reproducibility results. Moreover, it may also not be an appropriate reference in case of white matter pathology.ConclusionPoor post‐processing calibration options can lead to large errors in perfusion quantification, and a complete description of the calibration procedure should therefore be reported in ASL studies. Overall, our results further support the voxelwise calibration method proposed by the ASL white paper, particularly given the advantage of being relatively simple to implement and intrinsically correcting for the coil sensitivity profile

A systematic study of the sensitivity of partial volume correction methods for the quantification of perfusion from pseudo-continuous arterial spin labeling MRI

Arterial spin labeling (ASL) MRI is a non-invasive technique for the quantification of cerebral perfusion, and pseudo-continuous arterial spin labeling (PCASL) has been recommended as the standard implementation by a recent consensus of the community. Due to the low spatial resolution of ASL images, perfusion quantification is biased by partial volume effects. Consequently, several partial volume correction (PVEc) methods have been developed to reduce the bias in gray matter (GM) perfusion quantification. The efficacy of these methods relies on both the quality of the ASL data and the accuracy of partial volume estimates. Here we systematically investigate the sensitivity of different PVEc methods to variability in both the ASL data and partial volume estimates using simulated PCASL data and in vivo PCASL data from a reproducibility study. We examined the PVEc methods in two ways: the ability to preserve spatial details and the accuracy of GM perfusion estimation. Judging by the root-mean-square error (RMSE) between simulated and estimated GM CBF, the spatially regularized method was superior in preserving spatial details compared to the linear regression method (RMSE of 1.2 vs 5.1 in simulation of GM CBF with short scale spatial variations). The linear regression method was generally less sensitive than the spatially regularized method to noise in data and errors in the partial volume estimates (RMSE 6.3 vs 23.4 for SNR = 5 simulated data), but this could be attributed to the greater smoothing introduced by the method. Analysis of a healthy cohort dataset indicates that PVEc, using either method, improves the repeatability of perfusion quantification (within-subject coefficient of variation reduced by 5% after PVEc)

Defining the functional role of NaV1.7 in human nociception

Loss-of-function mutations in NaV1.7 cause congenital insensitivity to pain (CIP); this voltage-gated sodium channel is therefore a key target for analgesic drug development. Utilizing a multi-modal approach, we investigated how NaV1.7 mutations lead to human pain insensitivity. Skin biopsy and microneurography revealed an absence of C-fiber nociceptors in CIP patients, reflected in a reduced cortical response to capsaicin on fMRI. Epitope tagging of endogenous NaV1.7 revealed the channel to be localized at the soma membrane, axon, axon terminals, and the nodes of Ranvier of induced pluripotent stem cell (iPSC) nociceptors. CIP patient-derived iPSC nociceptors exhibited an inability to properly respond to depolarizing stimuli, demonstrating that NaV1.7 is a key regulator of excitability. Using this iPSC nociceptor platform, we found that some NaV1.7 blockers undergoing clinical trials lack specificity. CIP, therefore, arises due to a profound loss of functional nociceptors, which is more pronounced than that reported in rodent models, or likely achievable following acute pharmacological blockade

Stratifying patients with peripheral neuropathic pain based on sensory profiles : algorithm and sample size recommendations

In a recent cluster analysis, it has been shown that patients with peripheral neuropathic pain can be grouped into 3 sensory phenotypes based on quantitative sensory testing profiles, which are mainly characterized by either sensory loss, intact sensory function and mild thermal hyperalgesia and/or allodynia, or loss of thermal detection and mild mechanical hyperalgesia and/or allodynia. Here, we present an algorithm for allocation of individual patients to these subgroups. The algorithm is nondeterministic-ie, a patient can be sorted to more than one phenotype-and can separate patients with neuropathic pain from healthy subjects (sensitivity: 78%, specificity: 94%). We evaluated the frequency of each phenotype in a population of patients with painful diabetic polyneuropathy (n = 151), painful peripheral nerve injury (n = 335), and postherpetic neuralgia (n = 97) and propose sample sizes of study populations that need to be screened to reach a subpopulation large enough to conduct a phenotype-stratified study. The most common phenotype in diabetic polyneuropathy was sensory loss (83%), followed by mechanical hyperalgesia (75%) and thermal hyperalgesia (34%, note that percentages are overlapping and not additive). In peripheral nerve injury, frequencies were 37%, 59%, and 50%, and in postherpetic neuralgia, frequencies were 31%, 63%, and 46%. For parallel study design, either the estimated effect size of the treatment needs to be high (> 0.7) or only phenotypes that are frequent in the clinical entity under study can realistically be performed. For crossover design, populations under 200 patients screened are sufficient for all phenotypes and clinical entities with a minimum estimated treatment effect size of 0.5.Peer reviewe

Investigation of the neural correlates of ongoing pain states using quantitative perfusion arterial spin labelling

At present, there are few clinically effective pain therapies available to treat chronic pain. One reason is due to a lack of understanding about how pain emerges in the brain. Excitingly, an emerging body of work suggests that the perfusion imaging technique, arterial spin labelling (ASL), is particularly well-suited to investigate this issue. The primary aim of this thesis is to develop and optimise a quantitative perfusion imaging approach to investigate the neural correlates of both experimental and pathological tonic pain. In Chapter 2, we explore different methods of inducing ongoing pain in healthy subjects. Results from this study show that mechanically induced pain is well suited for use in ASL FMRI experiments. In Chapter 3, we compare currently available ASL FMRI approaches for investigating tonic states, using a range of sensory paradigms. Results from these experiments support the use of an optimised version of Continuous ASL (CASL) FMRI to obtain whote-brain perfusion. Additionally, we discuss our decision to proceed with the newly acquired pseudo-continuous ASL (pCASL); a novel ASL technique that benefits from maximal signal-to-noise (SNR) across a whole-brain volume. In Chapter 4 we implement the pCASL FMRI approach to image the neural correlates of ongoing experimental pain. Results from the investigation of parametrically modulated ongoing mechanical pain show robust pain-related activation of key pain related regions that are monotonically active with an increase in stimulus intensity. Additionally, data from this experiment shows the presence of complex perfusion dynamics relative to pain worthy of further study. In Chapter 5, we optimised the pCASL sequence to obtain absolute perfusion Changes across the whole-brain volume, using multi-inversion times, so that we could investigate the perfusion dynamics observed in Chapter 4. Results show that absolute perfusion Changes during tonic pain are considerably less than for regions recruited during a non- pain task. Additionally, dynamic perfusion changes show complex stimulus responses across all active regions regardless of stimulus type. We conclude that while the technique is well suited to quantify absolute perfusion, the mechanisms underlying the dynamic changes in CBF (neuronal signal, neurovascular coupling) need further study. Finally, in Chapter 6, we implement the absolute perfusion approach developed in Chapter 5 to interrogate the neural correlates of the genetic pain disease, Erythromelalgia, and pleasurable relief. The results of this study show pain-related activation (and relief-induced reduction) of key pain-related regions. We conclude from these results that the ASL technique developed over the course of this thesis can be used to study a range of pain pathologies. Taken together, the results of this thesis document the development of a powerful perfusion imaging technique capable of quantifying absolute perfusion changes across a whole-brain volume. The data presented here from investigations of both experimental and pathological pain states supports the use of this technique in future tonic pain studies, as well as other neuroscience applications. We are confident that implementation of this imaging approach will provide integral insight into the mechanisms of ongoing pain states; and further the development of novel efficacious pain treatment options.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

The dorsal posterior insula is not an island in pain but subserves a fundamental role - Response to: “Evidence against pain specificity in the dorsal posterior insula” by Davis et al. [version 1; referees: 2 approved]

An interesting and valuable discussion has arisen from our recent article (Segerdahl, Mezue et al., 2015) and we are pleased here to have the opportunity to expand on the various points we made. Equally important, we wish to correct several important misunderstandings that were made by Davis and colleagues that possibly contributed to their concerns about power when assessing our paper (e.g. actual subject numbers used in control experiment and the reality of the signal-to-noise and sampling of the multi-TI technique we employed). Here, we clarify the methods and analysis plus discuss how we interpret the data in the Brief Communication noting that the extrapolation and inferences made by Davis and colleagues are not consistent with our report or necessarily, in our opinion, what the data supports. We trust this reassures the F1000Research readership regarding the robustness of our results and what we actually concluded in the paper regarding their possible meaning. We are pleased, though, that Davis and colleagues have used our article to raise an important discussion around pain perception, and here offer some further insights towards that broader discussion

Modelling subject variability in the spatial and temporal characteristics of functional modes

Recent work has highlighted the scale and ubiquity of subject variability in observations from functional MRI data (fMRI). Furthermore, it is highly likely that errors in the estimation of either the spatial presentation of, or the coupling between, functional regions can confound cross-subject analyses, making accurate and unbiased representations of functional data essential for interpreting any downstream analyses. Here, we extend the framework of probabilistic functional modes (PFMs) (Harrison et al., 2015) to capture cross-subject variability not only in the mode spatial maps, but also in the functional coupling between modes and in mode amplitudes. A new implementation of the inference now also allows for the analysis of modern, large-scale data sets, and the combined inference and analysis package, PROFUMO, is available from git.fmrib.ox.ac.uk/samh/profumo. A new implementation of the inference now also allows for the analysis of modern, large-scale data sets. Using simulated data, resting-state data from 1000 subjects collected as part of the Human Connectome Project (Van Essen et al., 2013), and an analysis of 14 subjects in a variety of continuous task-states (Kieliba et al., 2019), we demonstrate how PFMs are able to capture, within a single model, a rich description of how the spatio-temporal structure of resting-state fMRI activity varies across subjects. We also compare the new PFM model to the well established independent component analysis with dual regression (ICA-DR) pipeline. This reveals that, under PFM assumptions, much more of the (behaviorally relevant) cross-subject variability in fMRI activity should be attributed to the variability in spatial maps, and that, after accounting for this, functional coupling between modes primarily reflects current cognitive state. This has fundamental implications for the interpretation of cross-sectional studies of functional connectivity that do not capture cross-subject variability to the same extent as PFMs.ISSN:1053-8119ISSN:1095-957