Increased theta band EEG power in sickle cell disease patients

Michelle Case, Sina Shirinpour, Huishi Zhang, Yvonne H Datta, Stephen C Nelson, Karim T Sadak, Kalpna Gupta, Bin He, Michelle Case, Sina Shirinpour, Huishi Zhang, Yvonne H Datta, Stephen C Nelson, Karim T Sadak, Kalpna Gupta, Bin He

Abstract

Objective: Pain 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.

Methods: We 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.

Results: SCD 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.

Conclusion: The present results support the pain mechanism referred to as thalamocortical dysrhythmia. This mechanism causes increased theta power in patients.

Significance: Our 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.

Keywords: chronic pain; electroencephalography; electrophysiological source imaging; sickle cell disease; thalamocortical dysrhythmia.

Conflict of interest statement

Disclosure The authors report no conflicts of interest in this work.

Figures

Figure 1
Figure 1
Flowchart of subject recruitment. Healthy controls were recruited through fliers and patients were recruited through local hematologists. Four patients were excluded from the study because of poor quality of EEG recordings from three patients and one declined to participate. A total of fourteen healthy controls and twenty patients were analyzed in this study. Abbreviation: EEG, electroencephalography.
Figure 2
Figure 2
Diagram of analysis protocols. The EEG data were preprocessed to remove artifacts and then filtered into different frequency bands to perform power spectral analysis and ESI. A BEM realistic-shaped generic head model was used to image sources using the eLORETA algorithm. Group contrast images were found from comparing source maps of the control and patient groups. Abbreviations: BEM, boundary element method; EEG, electroencephalography; ESI, electrophysiological source imaging.
Figure 3
Figure 3
Summary of average power results for each frequency band. (A) Plot showing continuous average power across frequency spectrum from 1 to 50 Hz. The average values are shown for the controls and patients. The standard deviation is shown by the shaded regions. (B) Bar plot showing group averages for specific frequency bands. The bars show the group mean value and the error bars show the standard deviation. *p<0.05.
Figure 4
Figure 4
Scatter plots describing significant correlations in theta band of the patient group. (A) Plot showing a positive correlation between maximum peak values of theta band and ED visits in the past 2 years. The p-value is 0.03 and R2 is 0.50. (B) Plot showing a negative correlation between the COG in theta band and the ED visits in the past 2 years. The p-value is <0.001 and R2 is 0.72. Abbreviations: ED, emergency department; COG, center of gravity.
Figure 5
Figure 5
Contrast images of ESI results. (A) Results of contrasts in the theta band. The contrast results of “control>patient” are shown in orange/yellow and the contrast results of “patient>control” are shown in blue. The t-values are shown in color bars. Results displayed are p<0.05 (FDR corrected). (B) Results of contrasts in the beta2 band. The contrast results of “control>patient” are shown in orange/yellow and the contrast results of “patient>control” are shown in blue. The t-values are shown in color bars. Results displayed are p<0.05 (FDR corrected). Abbreviations: ESI, electrophysiological source imaging; FDR, false discovery rate.

References

    1. Gaskin DJ, Richard P. The economic costs of pain in the United States. J Pain. 2012;13(8):715–724.
    1. Apkarian AV, Baliki MN, Geha PY. Towards a theory of chronic pain. Prog Neurobiol. 2009;87(2):81–97.
    1. Ceko M, Bushnell MC, Fitzcharles M-A, Schweinhardt P. Fibromyalgia interacts with age to change the brain. NeuroImage Clin. 2013;3:249–260.
    1. Gustin SM, Peck CC, Cheney LB, Macey PM, Murray GM, Henderson LA. Pain and plasticity: is chronic pain always associated with somatosensory cortex activity and reorganization? J Neurosci. 2012;32(43):14874–14884.
    1. Lee MC, Tracey I. Imaging pain: a potent means for investigating pain mechanisms in patients. Br J Anaesth. 2013;111(1):64–72.
    1. Maeda Y, Kettner N, Sheehan J, et al. Altered brain morphometry in carpal tunnel syndrome is associated with median nerve pathology. NeuroImage Clin. 2013;2:313–319.
    1. Apkarian AV, Sosa Y, Sonty S, et al. Chronic back pain is associated with decreased prefrontal and thalamic gray matter density. J Neurosci. 2004;24(46):10410–10415.
    1. Baliki MN, Mansour AR, Baria AT, Apkarian AV. Functional reorganization of the default mode network across chronic pain conditions. PLos One. 2014;9(9):e106133.
    1. Geha PY, Baliki MN, Harden RN, Bauer WR, Parrish TB, Apkarian AV. The brain in chronic CRPS pain: abnormal gray-white matter interactions in emotional and autonomic regions. Neuron. 2008;60(4):570–581.
    1. May A. Chronic pain may change the structure of the brain. Pain. 2008;137(1):7–15.
    1. Minniti CP, Lu K, Groninger H. Pain in sickle cell disease #270. J Palliat Med. 2013;16(6):697–699.
    1. Platt OS, Thorington BD, Brambilla DJ, et al. Pain in sickle cell disease. N Engl J Med. 1991;325(1):11–16.
    1. Smith WR, Penberthy LT, Bovbjerg VE, et al. Daily assessment of pain in adults with sickle cell disease. Ann Intern Med. 2008;148(2):94–101.
    1. Ballas SK, Gupta K, Adams-Graves P. Sickle cell pain: a critical reappraisal. Blood. 2012;120(18):3647–3656.
    1. Benjamin LJ, Swinson GI, Nagel RL. Sickle cell anemia day hospital: an approach for the management of uncomplicated painful crises. Blood. 2000;95(4):1130–1136.
    1. Brandow AM, Panepinto JA. Clinical interpretation of quantitative sensory testing as a measure of pain sensitivity in patients with sickle cell disease. J Pediatr Hematol Oncol. 2016;38(4):288–293.
    1. Campbell CM, Carroll CP, Kiley K, et al. Quantitative sensory testing and pain-evoked cytokine reactivity: comparison of patients with sickle cell disease to healthy matched controls. Pain. 2016;157(4):949–956.
    1. Keller SD, Yang M, Treadwell MJ, Werner EM, Hassell KL. Patient reports of health outcome for adults living with sickle cell disease: development and testing of the ASCQ-Me item banks. Health Qual Life Outcomes. 2014;12:125.
    1. McClish DK, Levenson JL, Penberthy LT, et al. Gender differences in pain and healthcare utilization for adult sickle cell patients: the PiSCES project. J Womens Health. 2006;15(2):146–154.
    1. Treadwell MJ, Hassell K, Levine R, Keller S. Adult Sickle Cell Quality-of-Life Measurement Information System (ASCQ-Me): conceptual model based on review of the literature and formative research. Clin J Pain. 2014;30(10):902–914.
    1. Dampier CD, Smith WR, Wager CG, et al. IMPROVE trial: a randomized controlled trial of patient-controlled analgesia for sickle cell painful episodes: rationale, design challenges, initial experience, and recommendations for future studies. Clin Trials. 2013;10(2):319–331.
    1. Ballas SK. Sickle cell disease: current clinical management. Semin Hematol. 2001;38(4):307–314.
    1. Boord P, Siddall PJ, Tran Y, Herbert D, Middleton J, Craig A. Electroencephalographic slowing and reduced reactivity in neuropathic pain following spinal cord injury. Spinal Cord. 2007;46(2):118–123.
    1. Van den Broeke EN, Wilder-Smith OHG, van Goor H, Vissers KCP, van Rijn CM. Patients with persistent pain after breast cancer treatment show enhanced alpha activity in spontaneous EEG. Pain Med. 2013;14(12):1893–1899.
    1. Jensen MP, Sherlin LH, Gertz KJ, et al. Brain EEG activity correlates of chronic pain in persons with spinal cord injury: clinical implications. Spinal Cord. 2013;51(1):55–58.
    1. Sarnthein J, Stern J, Aufenberg C, Rousson V, Jeanmonod D. Increased EEG power and slowed dominant frequency in patients with neurogenic pain. Brain. 2006;129(1):55–64.
    1. de Vries M, Wilder-Smith OHG, Jongsma MLA, et al. Altered resting state EEG in chronic pancreatitis patients: toward a marker for chronic pain. J Pain Res. 2013;6:815–824.
    1. Schmidt S, Naranjo JR, Brenneisen C, et al. Pain ratings, psychological functioning and quantitative EEG in a controlled study of chronic back pain patients. PLos One. 2012;7(3):e31138.
    1. Case M, Zhang H, Mundahl J, et al. Characterization of functional brain activity and connectivity using EEG and fMRI in patients with sickle cell disease. NeuroImage Clin. 2017;14:1–17.
    1. Darbari DS, Hampson JP, Ichesco E, et al. Frequency of hospitalizations for pain and association with altered brain network connectivity in sickle cell disease. J Pain. 2015;16(11):1077–1086.
    1. Wang ZJ, Wilkie DJ, Molokie R. Neurobiological mechanisms of pain in sickle cell disease. Hematology Am Soc Hematol Educ Program. 2010;2010(1):403–408.
    1. Wilkie DJ, Molokie R, Boyd-Seal D, et al. Patient-reported outcomes: descriptors of nociceptive and neuropathic pain and barriers to effective pain management in adult outpatients with sickle cell disease. J Natl Med Assoc. 2010;102(1):18–27.
    1. Delorme A, Makeig S. EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J Neurosci Methods. 2004;134(1):9–21.
    1. Klimesch W. EEG alpha and theta oscillations reflect cognitive and memory performance: a review and analysis. Brain Res Rev. 1999;29(2–3):169–195.
    1. RStudio Team . RStudio: Integrated Development for R. Boston, MA: RStudio, Inc.; 2015. [Accessed September 03, 2015]. Available from:
    1. Krzywinski M, Altman N. Points of significance: nonparametric tests. Nat Methods. 2014;11(5):467–468.
    1. Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Ser B Methodol. 1995;57(1):289–300.
    1. Olejnik S, Algina J. Generalized eta and omega squared statistics: measures of effect size for some common research designs. Psychol Methods. 2003;8(4):434–447.
    1. Cohen J. Statistical Power Analysis for the Behavioral Sciences. Hills-dale, NJ: Lawrence Earlbaum Associates; 1988. pp. 20–26.
    1. Sawilowsky S. New effect size rules of thumb. J Mod Appl Stat Methods. 2009;8(2):597–599.
    1. Lakens D. Calculating and reporting effect sizes to facilitate cumulative science: a practical primer for t-tests and ANOVAs. Front Psychol. 2013;4:863.
    1. Darbari DS, Onyekwere O, Nouraie M, et al. Markers of severe vaso-occlusive painful episode frequency in children and adolescents with sickle cell anemia. J Pediatr. 2012;160(2):286–290.
    1. Oostenveld R, Fries P, Maris E, Schoffelen J-M. FieldTrip: open source software for advanced analysis of MEG, EEG, and invasive electrophysiological data. Comput Intell Neurosci. 2011;2011:156869.
    1. Hamalainen MS, Sarvas J. Realistic conductivity geometry model of the human head for interpretation of neuromagnetic data. IEEE Trans Biomed Eng. 1989;36(2):165–171.
    1. He B, Musha T, Okamoto Y, Homma S, Nakajima Y, Sato T. Electric dipole tracing in the brain by means of the boundary element method and its accuracy. IEEE Trans Biomed Eng. 1987;34(6):406–414.
    1. Lai Y, van Drongelen W, Ding L, et al. Estimation of in vivo human brain-to-skull conductivity ratio from simultaneous extra- and intra-cranial electrical potential recordings. Clin Neurophysiol. 2005;116(2):456–465.
    1. Oostendorp TF, Delbeke J, Stegeman DF. The conductivity of the human skull: results of in vivo and in vitro measurements. IEEE Trans Biomed Eng. 2000;47(11):1487–1492.
    1. Holmes CJ, Hoge R, Collins L, Woods R, Toga AW, Evans AC. Enhancement of MR images using registration for signal averaging. J Comput Assist Tomogr. 1998;22(2):324–333.
    1. Pascual-Marqui RD. Discrete, 3D distributed, linear imaging methods of electric neuronal activity. Part 1: exact, zero error localization. ArXiv07103341 (math-ph) 2007. [Accessed May 12, 2017]. Available from: .
    1. Frei E, Gamma A, Pascual-Marqui R, Lehmann D, Hell D, Vollenweider FX. Localization of MDMA-induced brain activity in healthy volunteers using low resolution brain electromagnetic tomography (LORETA) Hum Brain Mapp. 2001;14(3):152–165.
    1. Jatoi MA, Kamel N, Malik AS, Faye I, Begum T. A survey of methods used for source localization using EEG signals. Biomed Signal Process Control. 2014;11:42–52.
    1. Pascual-Marqui RD, Lehmann D, Koukkou M, et al. Assessing interactions in the brain with exact low-resolution electromagnetic tomography. Philos Trans R Soc Lond Math Phys Eng Sci. 2011;369(1952):3768–3784.
    1. Ashburner J, Barnes G, Chen C, et al. SPM8 Manual. London, UK: Wellcome Trust Centre for Neuroimaging Institute of Neurology; 2012.
    1. Apkarian AV, Bushnell MC, Treede R-D, Zubieta J-K. Human brain mechanisms of pain perception and regulation in health and disease. Eur J Pain. 2005;9(4):463–484.
    1. Stern J, Jeanmonod D, Sarnthein J. Persistent EEG overactivation in the cortical pain matrix of neurogenic pain patients. NeuroImage. 2006;31(2):721–731.
    1. Huishi Zhang C, Sohrabpour A, Lu Y, He B. Spectral and spatial changes of brain rhythmic activity in response to the sustained thermal pain stimulation. Hum Brain Mapp. 2016;37(8):2976–2991.
    1. Roberts K, Papadaki A, Gonçalves C, et al. Contact heat evoked potentials using simultaneous EEG and fMRI and their correlation with evoked pain. BMC Anesthesiol. 2008;8:8.
    1. Farmer MA, Baliki MN, Apkarian AV. A dynamic network perspective of chronic pain. Neurosci Lett. 2012;520(2):197–203.
    1. Emmert K, Breimhorst M, Bauermann T, Birklein F, Van De Ville D, Haller S. Comparison of anterior cingulate vs. insular cortex as targets for real-time fMRI regulation during pain stimulation. Front Behav Neurosci. 2014;8:350.
    1. Stancak A, Fallon N. Emotional modulation of experimental pain: a source imaging study of laser evoked potentials. Front Hum Neurosci. 2013;7:552.
    1. Bush G, Luu P, Posner MI. Cognitive and emotional influences in anterior cingulate cortex. Trends Cogn Sci. 2000;4(6):215–222.
    1. Dolan RJ. Emotion, cognition, and behavior. Science. 2002;298(5596):1191–1194.
    1. Miller EK, Cohen JD. An integrative theory of prefrontal cortex function. Annu Rev Neurosci. 2001;24(1):167–202.
    1. Olesen SS, Hansen TM, Graversen C, Steimle K, Wilder-Smith OHG, Drewes AM. Slowed EEG rhythmicity in patients with chronic pancreatitis: evidence of abnormal cerebral pain processing? Eur J Gastroenterol Hepatol. 2011;23(5):418–424.
    1. Chen ACN, Feng W, Zhao H, Yin Y, Wang P. EEG default mode network in the human brain: spectral regional field powers. NeuroImage. 2008;41(2):561–574.
    1. Yang YM, Shah AK, Watson M, Mankad VN. Comparison of costs to the health sector of comprehensive and episodic health care for sickle cell disease patients. Public Health Rep. 1995;110(1):80–86.
    1. Aisiku IP, Smith WR, McClish DK, et al. Comparisons of high versus low emergency department utilizers in sickle cell disease. Ann Emerg Med. 2009;53(5):587–593.
    1. Yusuf HR, Atrash HK, Grosse SD, Parker CS, Grant AM. Emergency department visits made by patients with sickle cell disease: a descriptive study, 1999–2007. Am J Prev Med. 2010;38(Suppl 4):S536–S541.
    1. Woods K, Karrison T, Koshy M, Patel A, Friedmann P, Cassel C. Hospital utilization patterns and costs for adult sickle cell patients in Illinois. Public Health Rep. 1997;112(1):44–51.
    1. Pascoal-Faria P, Yalcin N, Fregni F. Neural markers of neuropathic pain associated with maladaptive plasticity in spinal cord injury. Pain Pract. 2015;15(4):371–377.
    1. Daniel LC, Li Y, Smith K, et al. Lessons learned from a randomized controlled trial of a family-based intervention to promote school functioning for school-age children with sickle cell disease. J Pediatr Psychol. 2015;40(10):1085–1094.
    1. Nimmer M, Czachor J, Turner L, et al. for the sickle cell working group of the Pediatric Emergency Care Applied Research Network (PECARN) The benefits and challenges of preconsent in a multisite, pediatric sickle cell intervention trial. Pediatr Blood Cancer. 2016;63(9):1649–1652.
    1. Nottage KA, Hankins JS, Faughnan LG, et al. Addressing challenges of clinical trials in acute pain: the Pain Management of Vaso-occlusive Crisis in Children and Young Adults with Sickle Cell Disease Study. Clin Trials. 2016;13(4):409–416.
    1. Patterson CA, Chavez V, Mondestin V, Deatrick J, Li Y, Barakat LP. Clinical trial decision making in pediatric sickle cell disease: a qualitative study of perceived benefits and barriers to participation. J Pediatr Hematol Oncol. 2015;37(6):415–422.
    1. Stevens EM, Patterson CA, Li YB, Smith-Whitley K, Barakat LP. Mistrust of pediatric sickle cell disease clinical trials research. Am J Prev Med. 2016;51(Suppl 1):S78–S86.
    1. Zou P, Helton KJ, Smeltzer M, et al. Hemodynamic responses to visual stimulation in children with sickle cell anemia. Brain Imaging Behav. 2011;5(4):295–306.
    1. Bush AM, Borzage MT, Choi S, et al. Determinants of resting cerebral blood flow in sickle cell disease. Am J Hematol. 2016;91(9):912–917.
    1. Jordan LC, Gindville MC, Scott AO, et al. Non-invasive imaging of oxygen extraction fraction in adults with sickle cell anaemia. Brain. 2016;139(Pt 3):738–750.
    1. He B, Yang L, Wilke C, Yuan H. Electrophysiological imaging of brain activity and connectivity – challenges and opportunities. IEEE Trans Biomed Eng. 2011;58(7):1918–1931.
    1. Zhang CH, Sha Z, Mundahl J, et al. Thalamocortical relationship in epileptic patients with generalized spike and wave discharges — a multimodal neuroimaging study. NeuroImage Clin. 2015;9:117–127.
    1. Shankar SM, Arbogast PG, Mitchel E, Cooper WO, Wang WC, Griffin MR. Medical care utilization and mortality in sickle cell disease: a population-based study. Am J Hematol. 2005;80(4):262–270.
    1. Ballas SK, Bauserman RL, McCarthy WF, Castro OL, Smith WR, Waclawiw MA. Hydroxyurea and acute painful crises in sickle cell anemia: effects on hospital length of stay and opioid utilization during hospitalization, outpatient acute care contacts, and at home. J Pain Symptom Manage. 2010;40(6):870–882.
    1. Colombatti R, Ermani M, Rampazzo P, et al. Cognitive evoked potentials and neural networks are abnormal in children with sickle cell disease and not related to the degree of anaemia, pain and silent infarcts. Br J Haematol. 2015;169(4):597–600.
    1. Caviness JN, Hentz JG, Belden CM, et al. Longitudinal EEG changes correlate with cognitive measure deterioration in Parkinson’s disease. J Park Dis. 2015;5(1):117–124.
    1. Hardmeier M, Hatz F, Penner IK, et al. Graph measures to characterize resting state connectomes in MS patients: an EEG-study over two years. Clin Neurophysiol. 2016;127(3):e38.
    1. He B, Sohrabpour A. Imaging epileptogenic brain using high density EEG source imaging and MRI. Clin Neurophysiol. 2016;127(1):5–7.
    1. Sohrabpour A, Lu Y, Worrell G, He B. Imaging brain source extent from EEG/MEG by means of an iteratively reweighted edge sparsity minimization (IRES) strategy. NeuroImage. 2016;142:27–42.

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