A Bayesian Neural Network-Based Method For The Extraction Of A Metabolite Corrected Arterial Input Function From Dynamic [11C]PBR28 PET

In PET, arterial sampling and metabolite correction are prerequisites for the gold-standard measurement of values like the volume of distribution (V T ), often necessary for the full quantification of radioligand binding. However, the invasiveness and technical demands of these procedures limit their application in both research and clinical PET studies. Machine learning approaches have been explored to predict V T from PET images, but their integration in clinical routine is limited by their lack of transparency or thorough evaluation. Here we propose a Bayesian Neural Network to estimate the arterial input function (AIF), while also outputting its prediction uncertainty, 1) directly from the entire dynamic PET images (NN-AEIF), 2) from an image-derived input function (IDIF)(NN-IDIF) and, as a sensitivity measure, 3) from the un-corrected plasma curve (NN-AIF). All methods, applied on [ 11 C]PBR28 PET data, were compared to the metabolite-corrected AIF in terms of V T , and the prediction uncertainty was assessed in terms of normalised coefficient of variance (nCV). Overall, both NN-AEIF and NN-AIF were able to accurately predict V T , outperforming the other methods, with NN-AEIF showing the lowest nCV

Neuro-immune signatures in chronic low back pain subtypes

We recently showed that patients with different chronic pain conditions (such as chronic low back pain, fibromyalgia, migraine, and Gulf War Illness) demonstrated elevated brain and/or spinal cord levels of the glial marker 18 kDa translocator protein, which suggests that neuroinflammation might be a pervasive phenomenon observable across multiple etiologically heterogeneous pain disorders. Interestingly, the spatial distribution of this neuroinflammatory signal appears to exhibit a degree of disease specificity (e.g. with respect to the involvement of the primary somatosensory cortex), suggesting that different pain conditions may exhibit distinct “neuroinflammatory signatures”. To further explore this hypothesis, we tested whether neuroinflammatory signal can characterize putative etiological subtypes of chronic low back pain patients based on clinical presentation. Specifically, we explored neuroinflammation in patients whose chronic low back pain either did or did not radiate to the leg (i.e. “radicular” vs. “axial” back pain). Fifty-four chronic low back pain patients, twenty-six with axial back pain (43.7 ± 16.6 y.o. [mean±SD]) and twenty-eight with radicular back pain (48.3 ± 13.2 y.o.), underwent PET/MRI with [11C]PBR28, a second-generation radioligand for the 18 kDa translocator protein. [11C]PBR28 signal was quantified using standardized uptake values ratio (validated against volume of distribution ratio; n = 23). Functional MRI data were collected simultaneously to the [11C]PBR28 data 1) to functionally localize the primary somatosensory cortex back and leg subregions and 2) to perform functional connectivity analyses (in order to investigate possible neurophysiological correlations of the neuroinflammatory signal). PET and functional MRI measures were compared across groups, cross-correlated with one another and with the severity of “fibromyalgianess” (i.e. the degree of pain centralization, or “nociplastic pain”). Furthermore, statistical mediation models were employed to explore possible causal relationships between these three variables. For the primary somatosensory cortex representation of back/leg, [11C]PBR28 PET signal and functional connectivity to the thalamus were: 1) higher in radicular compared to axial back pain patients, 2) positively correlated with each other and 3) positively correlated with fibromyalgianess scores, across groups. Finally, 4) fibromyalgianess mediated the association between [11C]PBR28 PET signal and primary somatosensory cortex-thalamus connectivity across groups. Our findings support the existence of “neuroinflammatory signatures” that are accompanied by neurophysiological changes, and correlate with clinical presentation (in particular, with the degree of nociplastic pain) in chronic pain patients. These signatures may contribute to the subtyping of distinct pain syndromes and also provide information about inter-individual variability in neuro-immune brain signals, within diagnostic groups, that could eventually serve as targets for mechanism-based precision medicine approaches

Response to peripheral immune stimulation within the brain: magnetic resonance imaging perspective of treatment success

Chronic peripheral inflammation in diseases such as rheumatoid arthritis leads to alterations in central pain processing and consequently to mood disorders resulting from sensitization within the central nervous system and enhanced vulnerability of the medial pain pathway. Proinflammatory cytokines such as tumor necrosis factor (TNF) alpha play an important role herein, and therapies targeting their signaling (i.e., anti-TNF therapies) have been proven to achieve good results. However, the phenomenon of rapid improvement in the patients’ subjective feeling after the start of TNFα neutralization remained confusing, because it was observed long before any detectable signs of inflammation decline. Functional magnetic resonance imaging (fMRI), enabling visualization of brain activity upon peripheral immune stimulation with anti-TNF, has helped to clarify this discrepancy. Moreover, fMRI appeared to work as a reliable tool for predicting prospective success of anti-TNF therapy, which is valuable considering the side effects of the drugs and the high therapy costs. This review, which is mainly guided by neuroimaging studies of the brain, summarizes the state-of-the-art knowledge about communication between the immune system and the brain and its impact on subjective well-being, addresses in more detail the outcome of the abovementioned anti-TNF fMRI studies (rapid response to TNFα blockade within the brain pain matrix and differences in brain activation patterns between prospective therapy responders and nonresponders), and discusses possible mechanisms for the latter phenomena and the predictive power of fMRI

Imaging clinically relevant pain states using arterial spin labeling

Arterial Spin Labeling (ASL) is a perfusion-based functional magnetic resonance imaging technique that uses water in arterial blood as a freely diffusible tracer to measure regional cerebral blood flow (rCBF) noninvasively. To date its application to the study of pain has been relatively limited. Yet, ASL possesses key features that make it uniquely positioned to study pain in certain paradigms. For instance, ASL is sensitive to very slowly fluctuating brain signals (in the order of minutes or longer). This characteristic makes ASL particularly suitable to the evaluation of brain mechanisms of tonic experimental, post-surgical and ongoing/or continuously varying pain in chronic or acute pain conditions (whereas BOLD fMRI is better suited to detect brain responses to short-lasting or phasic/evoked pain). Unlike positron emission tomography or other perfusion techniques, ASL allows the estimation of rCBF without requiring the administration of radioligands or contrast agents. Thus, ASL is well suited for within-subject longitudinal designs (e.g., to study evolution of pain states over time, or of treatment effects in clinical trials). ASL is also highly versatile, allowing for novel paradigms exploring a flexible array of pain states, plus it can be used to simultaneously estimate not only pain-related alterations in perfusion but also functional connectivity. In conclusion, ASL can be successfully applied in pain paradigms that would be either challenging or impossible to implement using other techniques. Particularly when used in concert with other neuroimaging techniques, ASL can be a powerful tool in the pain imager's toolbox