Pretreatment Brain White Matter Integrity Associated With Neuropathic Pain Relief and Changes in Temporal Summation of Pain Following Ketamine.

Neuropathic pain (NP) is a prevalent condition often associated with heightened pain responsiveness suggestive of central sensitization. Neuroimaging biomarkers of treatment outcomes may help develop personalized treatment strategies, but white matter (WM) properties have been underexplored for this purpose. Here we assessed whether WM pathways of the default mode network (DMN: medial prefrontal cortex [mPFC], posterior cingulate cortex, and precuneus) and descending pain modulation system (periaqueductal gray [PAG]) are associated with ketamine analgesia and attenuated temporal summation of pain (TSP, reflecting central sensitization) in NP. We used a fixel-based analysis of diffusion-weighted imaging data to evaluate WM microstructure (fiber density [FD]) and macrostructure (fiber bundle cross-section) within the DMN and mPFC-PAG pathways in 70 individuals who underwent magnetic resonance imaging and TSP testing; 35 with NP who underwent ketamine treatment and 35 age- and sex-matched pain-free individuals. Individuals with NP were assessed before and 1 month after treatment; those with ≥30% pain relief were considered responders (n = 18), or otherwise as nonresponders (n = 17). We found that WM structure within the DMN and mPFC-PAG pathways did not differentiate responders from nonresponders. However, pretreatment FD in the anterior limb of the internal capsule correlated with pain relief (r=.48). Moreover, pretreatment FD in the DMN (left mPFC-precuneus/posterior cingulate cortex; r=.52) and mPFC-PAG (r=.42) negatively correlated with changes in TSP. This suggests that WM microstructure in the DMN and mPFC-PAG pathway is associated with the degree to which ketamine reduces central sensitization. Thus, fixel metrics of WM structure may hold promise to predict ketamine NP treatment outcomes. PERSPECTIVE: We used advanced fixel-based analyses of MRI diffusion-weighted imaging data to identify pretreatment WM microstructure associated with ketamine outcomes, including analgesia and markers of attenuated central sensitization. Exploring associations between brain structure and treatment outcomes could contribute to a personalized approach to treatment for individuals with NP.

The Potential Clinical Utility of Pressure-Based vs. Heat-Based Paradigms to Measure Conditioned Pain Modulation in Healthy Individuals and Those With Chronic Pain.

Conditioned pain modulation (CPM) is a physiological measure thought to reflect an individual's endogenous pain modulation system. CPM varies across individuals and provides insight into chronic pain pathophysiology. There is growing evidence that CPM may help predict individual pain treatment outcome. However, paradigm variabilities and practical issues have impeded widespread clinical adoption of CPM assessment. This study aimed to compare two CPM paradigms in people with chronic pain and healthy individuals. A total of 30 individuals (12 chronic pain, 18 healthy) underwent two CPM paradigms. The heat CPM paradigm acquired pain intensity ratings evoked by a test stimulus (TS) applied before and during the conditioning stimulus (CS). The pressure CPM paradigm acquired continuous pain intensity ratings of a gradually increasing TS, before and during CS. Pain intensity was rated from 0 (no pain) to 100 (worst pain imaginable); Pain50 is the stimulus level for a response rated 50. Heat and pressure CPM were calculated as a change in TS pain intensity ratings at Pain50, where negative CPM scores indicate pain inhibition. We also determined CPM in the pressure paradigm as change in pressure pain detection threshold (PDT). We found that in healthy individuals the CPM effect was significantly more inhibitory using the pressure paradigm than the heat paradigm. The pressure CPM effect was also significantly more inhibitory when based on changes at Pain50 than at PDT. However, in individuals with chronic pain there was no significant difference in pressure CPM compared to heat or PDT CPM. There was no significant correlation between clinical pain measures (painDETECT and Brief Pain Inventory) and paradigm type (heat vs. pressure), although heat-based CPM and painDETECT scores showed a trend. Importantly, the pressure paradigm could be administered in less time than the heat paradigm. Thus, our study indicates that in healthy individuals, interpretation of CPM findings should consider potential modality-dependent effects. However, in individuals with chronic pain, either heat or pressure paradigms can similarly be used to assess CPM. Given the practical advantages of the pressure paradigm (e.g., short test time, ease of use), we propose this approach to be well-suited for clinical adoption.

High-throughput drone-based remote sensing reliably tracks phenology in thousands of conifer seedlings.

Phenology is an important indicator of environmental variation and climate change impacts on tree responses. In conifers, monitoring phenology of photosynthesis through remote sensing has been unreliable, because needle foliage varies little throughout the year. This is challenging for modelling ecosystem carbon uptake and monitoring phenology for enhanced breeding (genomic selection) and forest health. Here, we demonstrate that drone-based carotenoid-sensitive spectral indices, such as the Chl/carotenoid index (CCI), can be used to track phenology in conifers by taking advantage of the close relationship between seasonally changing carotenoid levels and the variation of photosynthetic activity. Physiological ground measurements, including photosynthetic pigments and maximum quantum yield of Chl fluorescence, indicated that CCI tracked the variation of photosynthetic activity better than other vegetation indices for 30 white spruce seedlings measured over 1 yr. A machine-learning approach, using CCI derived from drone-based multispectral imagery, was used to model phenology of photosynthesis for the entire pedigree population (6000 seedlings). This high-throughput drone-based phenotyping approach is suitable for studying climate change impacts and environmental variation on the physiological status of thousands of field-grown conifers at unprecedented speed and scale.