Sickle cell disease (SCD) is a red blood cell disorder that causes many complications including life-long pain. Treatment of pain remains challenging due to a poor understanding of the mechanisms and limitations to characterize and quantify pain. In the present study, we examined simultaneously recording functional MRI (fMRI) and electroencephalogram (EEG) to better understand neural connectivity as a consequence of chronic pain in SCD patients. We performed independent component analysis and seed-based connectivity on fMRI data. Spontaneous power and microstate analysis was performed on EEG-fMRI data. ICA analysis showed that patients lacked activity in the default mode network (DMN) and executive control network compared to controls. EEG-fMRI data revealed that the insula cortex's role in salience increases with age in patients. EEG microstate analysis showed patients had increased activity in pain processing regions. The cerebellum in patients showed a stronger connection to the periaqueductal gray matter (involved in pain inhibition), and negative connections to pain processing areas. These results suggest that patients have reduced activity of DMN and increased activity in pain processing regions during rest. The present findings suggest resting state connectivity differences between patients and controls can be used as novel biomarkers of SCD pain.

Objective Effective pain assessment and management strategies are needed to better manage pain. In addition to self-report, an objective pain assessment system can provide a more complete picture of the neurophysiological basis for pain. In this study, a robust and accurate machine learning approach is developed to quantify tonic thermal pain across healthy subjects into a maximum of ten distinct classes. Methods A random forest model was trained to predict pain scores using time-frequency wavelet representations of independent components obtained from electroencephalography (EEG) data, and the relative importance of each frequency band to pain quantification is assessed. Results The mean classification accuracy for predicting pain on an independent test subject for a range of 1–10 is 89.45%, highest among existing state of the art quantification algorithms for EEG. The gamma band is the most important to both inter-subject and intra-subject classification accuracy. Conclusion The robustness and generalizability of the classifier is demonstrated. Significance Our results demonstrate the potential of this tool to be used clinically to help improve chronic pain treatment, and establish spectral biomarkers for future pain-related studies using EEG.

Sickle cell disease (SCD) is a hereditary blood disorder associated with many life-threatening comorbidities including cerebral stroke and chronic pain. The long-term effects of this disease may therefore affect the global brain network which is not clearly understood. We performed graph theory analysis of functional networks using non-invasive fMRI and high resolution EEG on thirty-one SCD patients and sixteen healthy controls. Resting state data were analyzed to determine differences between controls and patients with less severe and more severe sickle cell related pain. fMRI results showed that patients with higher pain severity had lower clustering coefficients and local efficiency. The neural network of the more severe patient group behaved like a random network when performing a targeted attack network analysis. EEG results showed the beta1 band had similar results to fMRI resting state data. Our data show that SCD affects the brain on a global level and that graph theory analysis can differentiate between patients with different levels of pain severity.

ObjectivePain is a major issue in the care of patients with sickle cell disease (SCD). The mechanisms behind pain and the best way to treat it are not well understood. We studied how electroencephalography (EEG) is altered in SCD patients.MethodsWe recruited 20 SCD patients and compared their resting state EEG to that of 14 healthy controls. EEG power was found across frequency bands using Welch’s method. Electrophysiological source imaging was assessed for each frequency band using the eLORETA algorithm.ResultsSCD patients had increased theta power and decreased beta2 power compared to controls. Source localization revealed that areas of greater theta band activity were in areas related to pain processing. Imaging parameters were significantly correlated to emergency department visits, which indicate disease severity and chronic pain intensity.ConclusionThe present results support the pain mechanism referred to as thalamocortical dysrhythmia. This mechanism causes increased theta power in patients.SignificanceOur findings show that EEG can be used to quantitatively evaluate differences between controls and SCD patients. Our results show the potential of EEG to differentiate between different levels of pain in an unbiased setting, where specific frequency bands could be used as biomarkers for chronic pain.

Introduction One major challenge in the treatment of pain from sickle cell disease (SCD) is the current lack of an objective measure of pain. Therefore, we used functional magnetic imaging (fMRI) to compare a specific brain network in SCD patients with healthy subjects to develop objective methods to assess pain. We hypothesize that in SCD patients, the default-mode-network (DMN) is less active in comparison to healthy subjects. DMN is a prevalent network dynamic that appears in the absence of overt behavior and is thought to be responsible for a host of visceral mental activities. This DMN difference may be due to prolonged SCD-related pain. Methods Ten healthy subjects (6 males and 4 females; age: mean=23.3, SD= 3.3 years) and ten SCD patients (5 males, 5 females; age: mean= 28.5, SD=7.1 years) participated in the study following informed consent to the procedures approved by the IRB of the University of Minnesota. Patients were recruited by hematologists at the University of Minnesota Medical Center. None of the patients were experiencing acute crisis during the experiments. FMRI data was acquired with a 3T Siemens Trio whole-body scanner with echo-planar imaging (EPI) sequence. Each fMRI recording lasted about 6minutes. The experiment procedures were well tolerated by all subjects. Both independent component analysis (ICA) and seed-based region of interest (ROI) analysis were applied to the fMRI data, and the analysis was performed using the BrainVoyager QX software. Results Experimental and analyticalprocedures were applied to both groups under similar conditions and the recorded data in the two groups have comparable quality. Using the data driven ICA-based analysis, each fMRI data set was decomposed into thirty independent components. The DMN component waseasilyidentified in all of the ten healthy subjects. In contrast, none of the ten SCD patients had any identifiable DMN component in the ICA-based analysis. Seed-based ROI analysis was also performed to find correlational networks. The ROIs were predetermined to be in medial prefrontal cortex (mPFC), posterior cingulate cortex (PCC), left and right lateral parietal cortex (LP). Using the time course extracted from the ROIs, DMN was revealed in all ten healthy subjects. However, DMN can only be found in three SCD patients.The identified DMN in patients showed incomplete clusters and had smaller cluster size comparing with the DMNin healthy subjects. By examining different possible ROI locations, DMN identified in patients consistently showed smaller number of voxels compared to controls. Conclusions Our findings suggest that the neurological signature of SCD patients may be altered by the chronic painful condition caused by the disease. Diminished activity in the DMN during rest has been previously reported by studies on both cognitive impairments and other types of chronic pain. It is currently unclear whether synchrony among the nodes in the default mode network can be reestablished once the pain condition is alleviated. Knowledge of the neurological characteristics of SCD patients may shed light in understanding in disease and the role of pain in SCD. Changes in DMN activity may also serve as a potential biomarker to quantify pain severity in the future.(This work was supported in part by NIH U01 HL117664 and NSF DGE-1069104.) References Baliki, M.N. (2008), ‘Beyond feeling: chronic pain hurts the brain, disrupting the default-mode network dynamics’, The Journal of Neuroscience, vol. 28, no. 6, pp. 1398-1403. Fox, M.D. (2005), ‘The human brain is intrinsically organized into dynamic, anticorrelated functional networks’,ProcNatlAcadSci USA, vol. 102, pp. 9673–9678. Disclosures No relevant conflicts of interest to declare.