Neuroinflammation in post-acute sequelae of COVID-19 (PASC) as assessed by [11C]PBR28 PET correlates with vascular disease measures.

The COVID-19 pandemic caused by SARS-CoV-2 has triggered a consequential public health crisis of post-acute sequelae of COVID-19 (PASC), sometimes referred to as long COVID. The mechanisms of the heterogeneous persistent symptoms and signs that comprise PASC are under investigation, and several studies have pointed to the central nervous and vascular systems as being potential sites of dysfunction. In the current study, we recruited individuals with PASC with diverse symptoms, and examined the relationship between neuroinflammation and circulating markers of vascular dysfunction. We used [11C]PBR28 PET neuroimaging, a marker of neuroinflammation, to compare 12 PASC individuals versus 43 normative healthy controls. We found significantly increased neuroinflammation in PASC versus controls across a wide swath of brain regions including midcingulate and anterior cingulate cortex, corpus callosum, thalamus, basal ganglia, and at the boundaries of ventricles. We also collected and analyzed peripheral blood plasma from the PASC individuals and found significant positive correlations between neuroinflammation and several circulating analytes related to vascular dysfunction. These results suggest that an interaction between neuroinflammation and vascular health may contribute to common symptoms of PASC.

Neuroimmune activation and increased brain aging in chronic pain patients after the COVID-19 pandemic onset.

The COVID-19 pandemic has exerted a global impact on both physical and mental health, and clinical populations have been disproportionally affected. To date, however, the mechanisms underlying the deleterious effects of the pandemic on pre-existing clinical conditions remain unclear. Here we investigated whether the onset of the pandemic was associated with an increase in brain/blood levels of inflammatory markers and MRI-estimated brain age in patients with chronic low back pain (cLBP), irrespective of their infection history. A retrospective cohort study was conducted on 56 adult participants with cLBP (28 'Pre-Pandemic', 28 'Pandemic') using integrated Positron Emission Tomography/ Magnetic Resonance Imaging (PET/MRI) and the radioligand [11C]PBR28, which binds to the neuroinflammatory marker 18 kDa Translocator Protein (TSPO). Image data were collected between November 2017 and January 2020 ('Pre-Pandemic' cLBP) or between August 2020 and May 2022 ('Pandemic' cLBP). Compared to the Pre-Pandemic group, the Pandemic patients demonstrated widespread and statistically significant elevations in brain TSPO levels (P =.05, cluster corrected). PET signal elevations in the Pandemic group were also observed when 1) excluding 3 Pandemic subjects with a known history of COVID infection, or 2) using secondary outcome measures (volume of distribution -VT- and VT ratio - DVR) in a smaller subset of participants. Pandemic subjects also exhibited elevated serum levels of inflammatory markers (IL-16; P <.05) and estimated BA (P <.0001), which were positively correlated with [11C]PBR28 SUVR (r's ≥ 0.35; P's < 0.05). The pain interference scores, which were elevated in the Pandemic group (P <.05), were negatively correlated with [11C]PBR28 SUVR in the amygdala (r = -0.46; P<.05). This work suggests that the pandemic outbreak may have been accompanied by neuroinflammation and increased brain age in cLBP patients, as measured by multimodal imaging and serum testing. This study underscores the broad impact of the pandemic on human health, which extends beyond the morbidity solely mediated by the virus itself.

Neuroimmune 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 (TSPO), which suggests that neuroinflammation might be a pervasive phenomenon observable across multiple aetiologically 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 explore this hypothesis further, we tested whether neuroinflammatory signal can characterize putative aetiological 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' versus 'axial' back pain). Fifty-four patients with chronic low back pain, 26 with axial back pain [43.7 ± 16.6 years old (mean ± SD)] and 28 with radicular back pain (48.3 ± 13.2 years old), underwent PET/MRI with 11C-PBR28, a second-generation radioligand for TSPO. 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 (i) to functionally localize the primary somatosensory cortex back and leg subregions; and (ii) 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 used 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: (i) higher in radicular compared to axial back pain patients; (ii) positively correlated with each other; (iii) positively correlated with fibromyalgianess scores, across groups; and finally (iv) 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 interindividual variability in neuroimmune brain signals, within diagnostic groups, that could eventually serve as targets for mechanism-based precision medicine approaches.

The pandemic brain: Neuroinflammation in non-infected individuals during the COVID-19 pandemic.

While COVID-19 research has seen an explosion in the literature, the impact of pandemic-related societal and lifestyle disruptions on brain health among the uninfected remains underexplored. However, a global increase in the prevalence of fatigue, brain fog, depression and other "sickness behavior"-like symptoms implicates a possible dysregulation in neuroimmune mechanisms even among those never infected by the virus. We compared fifty-seven 'Pre-Pandemic' and fifteen 'Pandemic' datasets from individuals originally enrolled as control subjects for various completed, or ongoing, research studies available in our records, with a confirmed negative test for SARS-CoV-2 antibodies. We used a combination of multimodal molecular brain imaging (simultaneous positron emission tomography / magnetic resonance spectroscopy), behavioral measurements, imaging transcriptomics and serum testing to uncover links between pandemic-related stressors and neuroinflammation. Healthy individuals examined after the enforcement of 2020 lockdown/stay-at-home measures demonstrated elevated brain levels of two independent neuroinflammatory markers (the 18 kDa translocator protein, TSPO, and myoinositol) compared to pre-lockdown subjects. The serum levels of two inflammatory markers (interleukin-16 and monocyte chemoattractant protein-1) were also elevated, although these effects did not reach statistical significance after correcting for multiple comparisons. Subjects endorsing higher symptom burden showed higher TSPO signal in the hippocampus (mood alteration, mental fatigue), intraparietal sulcus and precuneus (physical fatigue), compared to those reporting little/no symptoms. Post-lockdown TSPO signal changes were spatially aligned with the constitutive expression of several genes involved in immune/neuroimmune functions. This work implicates neuroimmune activation as a possible mechanism underlying the non-virally-mediated symptoms experienced by many during the COVID-19 pandemic. Future studies will be needed to corroborate and further interpret these preliminary findings.

Data driven surrogate signal extraction for dynamic PET using selective PCA: time windows versus the combination of components.

Respiratory motion correction is beneficial in PET, as it can reduce artefacts caused by motion and improve quantitative accuracy. Methods of motion correction are commonly based on a respiratory trace obtained through an external device (like the Real Time Position Management System) or a data driven method, such as those based on dimensionality reduction techniques (for instance PCA). PCA itself being a linear transformation to the axis of greatest variation. Data driven methods have the advantage of being non-invasive, and can be performed post-acquisition. However, their main downside being that they are adversely affected by the tracer kinetics of the dynamic PET acquisition. Therefore, they are mostly limited to static PET acquisitions. This work seeks to extend on existing PCA-based data-driven motion correction methods, to allow for their applicability to dynamic PET imaging. The methods explored in this work include; a moving window approach (similar to the Kinetic Respiratory Gating method from Schleyer et al.), extrapolation of the principal component from later time points to earlier time points, and a method to score, select, and combine multiple respiratory components. The resulting respiratory traces were evaluated on 22 data sets from a dynamic 18FFDG study on patients with Idiopathic Pulmonary Fibrosis. This was achieved by calculating their correlation with a surrogate signal acquired using a Real Time Position Management System. The results indicate that all methods produce better surrogate signals than when applying conventional PCA to dynamic data (for instance, a higher correlation with a gold standard respiratory trace). Extrapolating a late time point principal component produced more promising results than using a moving window. Scoring, selecting, and combining components held benefits over all other methods. This work allows for the extraction of a surrogate signal from dynamic PET data earlier in the acquisition and with a greater accuracy than previous work. This potentially allows for numerous other methods (for instance, respiratory motion correction) to be applied to this data (when they otherwise could not be previously used).

Neuroinflammation in post-acute sequelae of COVID-19 (PASC) as assessed by [11C]PBR28 PET correlates with vascular disease measures.

The COVID-19 pandemic caused by SARS-CoV-2 has triggered a consequential public health crisis of post-acute sequelae of COVID-19 (PASC), sometimes referred to as long COVID. The mechanisms of the heterogeneous persistent symptoms and signs that comprise PASC are under investigation, and several studies have pointed to the central nervous and vascular systems as being potential sites of dysfunction. In the current study, we recruited individuals with PASC with diverse symptoms, and examined the relationship between neuroinflammation and circulating markers of vascular dysfunction. We used [11C]PBR28 PET neuroimaging, a marker of neuroinflammation, to compare 12 PASC individuals versus 43 normative healthy controls. We found significantly increased neuroinflammation in PASC versus controls across a wide swath of brain regions including midcingulate and anterior cingulate cortex, corpus callosum, thalamus, basal ganglia, and at the boundaries of ventricles. We also collected and analyzed peripheral blood plasma from the PASC individuals and found significant positive correlations between neuroinflammation and several circulating analytes related to vascular dysfunction. These results suggest that an interaction between neuroinflammation and vascular health may contribute to common symptoms of PASC.

An Investigation of Stochastic Variance Reduction Algorithms for Relative Difference Penalized 3D PET Image Reconstruction.

Penalised PET image reconstruction algorithms are often accelerated during early iterations with the use of subsets. However, these methods may exhibit limit cycle behaviour at later iterations due to variations between subsets. Desirable converged images can be achieved for a subclass of these algorithms via the implementation of a relaxed step size sequence, but the heuristic selection of parameters will impact the quality of the image sequence and algorithm convergence rates. In this work, we demonstrate the adaption and application of a class of stochastic variance reduction gradient algorithms for PET image reconstruction using the relative difference penalty and numerically compare convergence performance to BSREM. The two investigated algorithms are: SAGA and SVRG. These algorithms require the retention in memory of recently computed subset gradients, which are utilised in subsequent updates. We present several numerical studies based on Monte Carlo simulated data and a patient data set for fully 3D PET acquisitions. The impact of the number of subsets, different preconditioners and step size methods on the convergence of regions of interest values within the reconstructed images is explored. We observe that when using constant preconditioning, SAGA and SVRG demonstrate reduced variations in voxel values between subsequent updates and are less reliant on step size hyper-parameter selection than BSREM reconstructions. Furthermore, SAGA and SVRG can converge significantly faster to the penalised maximum likelihood solution than BSREM, particularly in low count data.

PET/MRI attenuation estimation in the lung: A review of past, present, and potential techniques.

Positron emission tomography/magnetic resonance imaging (PET/MRI) potentially offers several advantages over positron emission tomography/computed tomography (PET/CT), for example, no CT radiation dose and soft tissue images from MR acquired at the same time as the PET. However, obtaining accurate linear attenuation correction (LAC) factors for the lung remains difficult in PET/MRI. LACs depend on electron density and in the lung, these vary significantly both within an individual and from person to person. Current commercial practice is to use a single-valued population-based lung LAC, and better estimation is needed to improve quantification. Given the under-appreciation of lung attenuation estimation as an issue, the inaccuracy of PET quantification due to the use of single-valued lung LACs, the unique challenges of lung estimation, and the emerging status of PET/MRI scanners in lung disease, a review is timely. This paper highlights past and present methods, categorizing them into segmentation, atlas/mapping, and emission-based schemes. Potential strategies for future developments are also presented.