Brain Network Hypersensitivity Underlies Pain Crises in Sickle Cell Disease
Poster No:
1679
Submission Type:
Abstract Submission
Authors:
Pangyu Joo1, Uncheol Lee1, Richard Harris2, Ying Wang3
Institutions:
1Center for Consciousness Science, University of Michigan, Ann Arbor, MI, 2University of California at Irvine School of Medicine, Irvine, CA, 3Indiana University School of Medicine, Indianapolis, IN
First Author:
Co-Author(s):
Introduction:
Sickle cell disease (SCD) is a genetic disorder characterized by abnormally deformed red blood cells that block blood flow in small blood vessels and cause acute pain episodes, referred to as vascular occlusion crises (VOCs). The frequent and repeated pain episodes lower the pain threshold, resulting in increased pain hypersensitivity. In addition, the occurrence of VOC is repetitive and unpredictable, making timely implementation of preventive strategies challenging. Therefore, these raise two crucial questions: Is there a brain mechanism responsible for the pain hypersensitivity in SCD? Can we predict the upcoming VOCs? In a previous study, we proposed explosive synchronization (ES), a universal phenomenon wherein a small perturbation can abruptly trigger global synchronization within a network, as the hypersensitivity of fibromyalgia. Here we hypothesize that ES may also underlie the pain hypersensitivity of SCD. In light of the temporal progression of VOC, we expect that the condition of ES may develop progressively until VOC onset and diminish afterward.
Methods:
To test the hypotheses, we analyzed the EEG of SCD and developed a computational model. We recruited 24 SCD patients and 18 healthy control subjects for the EEG analysis. EEG recordings were acquired at rest and during painful pressure cuff application to the left calf. We examined the relationship between ES strength, measured by frequency disassortativity of the alpha EEG band (7-13Hz), patient-reported outcome measures (PROMs), and VOCs. Frequency disassortativity (FDA), a measure reflecting the proximity of a network to ES, indicates a tendency of higher frequency EEG channels linking to lower frequency EEG channels. Additionally, we designed a computational model with an anatomically informed human brain network structure and neural-mass models to simulate the interplay between ES strength in the brain network, brain sensitivity, and the occurrence of VOCs.
Results:
(EEG analysis) The SCD group exhibited a significantly lower median alpha frequency (9.01±0.09Hz) compared to the control group (9.83±0.13Hz) in the eyes-closed resting state. Under pain stimulation, the FDA of the alpha EEG band (measured by Spearman correlation between the median alpha frequencies of 31 EEG channels) for the SCD significantly correlated with three pain scores (PROMs): BPI Pain Interference score (R=-0.451, p=0.031), PROMISE29 Physical Function (R=-0.620, p=0.002), and HADS Depression (R=-0.482, p=0.020). These findings suggest that patients with greater pain, depression, and poor physical function displayed a stronger ES in the EEG network. Furthermore, patients with more frequent VOCs in the preceding 12 months presented with a larger ES strength (i.e., a larger FDA, R=-0.595, p=0.001). Importantly, the occurrence of the VOCs relative to the time of EEG recordings (within 30 days) was significantly correlated with the ES strength: the closer the occurrence of VOCs, the stronger the ES (i.e., a larger FDA) in EEG. (Computational model) Consistent with the EEG analysis results, increasing the FDA in the brain network model resulted in a higher brain network sensitivity to external perturbation, as measured by the responsivity and complexity of the perturbed signals. Additionally, the brains more frequently crossed over a threshold of FDA (set by the EEGs of VOC) under noisy environments.
Conclusions:
In summary, we presented first-hand evidence supporting ES as the underlying mechanism of pain hypersensitivity in SCD and proposed the frequency disassortativity (FDA) of median alpha frequencies, a measure of ES strength in EEG, as a promising indicator for assessing pain scores and predicting the occurrence of VOCs. Further research should focus on developing a mechanism-based brain modulation method to preempt VOCs.
Modeling and Analysis Methods:
EEG/MEG Modeling and Analysis 1
Perception, Attention and Motor Behavior:
Perception: Pain and Visceral 2
Keywords:
Computational Neuroscience
Electroencephaolography (EEG)
Modeling
Pain
1|2Indicates the priority used for review
Provide references using author date format
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