Central nervous system complications are common in sickle cell disease (SCD), and the defining associated biomarkers are becoming increasingly relevant for physicians in diagnostic and prognostic contexts. Recent studies have reported altered brain connectivity in pain processing, highlighting a new avenue for developing sensitive measures of SCD severity.
Method:
This cross-sectional study used graph theory concepts to analyze effective connectivity in individuals with SCD and healthy controls during rest and motor imagery tasks. The SCD group was further divided into two subgroups based on pain intensity (less pain or more pain) during the evaluation.
Results:
Individuals with SCD and chronic pain exhibited a distinct brain connectivity signature compared to healthy individuals and within pain sublevels.
Conclusion:
Chronic pain in SCD shows a unique brain connectivity pattern when compared to healthy subjects and across different pain levels. The results support the hypothesis that chronic pain condition is associated with decreased interhub connections and increased intrahub connections for specific brain rhythms. Furthermore, the small-world parameter can distinguish SCD individuals from controls and differentiate pain levels within SCD individuals, offering a promising biomarker for clinical assessment.
Impact Statement
Chronic pain exhibits a different brain connectivity signature compared to healthy subjects and across sublevels of pain. The results support the hypothesis that chronic pain is associated with decreased interhub connections and increased intrahub connections for specific brain rhythms.
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References
1.
BabiloniC, BrancucciA, Del PercioC, et al.Anticipatory electroencephalography alpha rhythm predicts subjective perception of pain intensity. J Pain, 2006; 7(10):709–717; doi: 10.1016/j.jpain.2006.03.005
2.
BabiloniC, CapotostoP, BrancucciA, et al.Cortical alpha rhythms are related to the anticipation of sensorimotor interaction between painful stimuli and movements: A high-resolution eeg study. J Pain, 2008; 9(10):902–911; doi: 10.1016/j.jpain.2008.05.007
3.
BarrosoJ, WakaizumiK, ReisAM, et al.Reorganization of functional brain network architecture in chronic osteoarthritis pain. Hum Brain Mapp, 2021; 42(4):1206–1222; doi: 10.1002/hbm.25287
4.
BarttfeldP, WickerB, CukierS, et al.A big-world network in asd: Dynamical connectivity analysis reflects a deficit in long-range connections and an excess of short-range connections. Neuropsychologia, 2011; 49(2):254–263; doi: 10.1016/j.neuropsychologia.2010.11.024
5.
BernhardtBC, BonilhaL, GrossDW. Network analysis for a network disorder: The emerging role of graph theory in the study of epilepsy. Epilepsy Behav, 2015; 50:162–170; doi: 10.1016/j.yebeh.2015.06.005
6.
BullmoreE, SpornsO. The economy of brain network organization. Nat Rev Neurosci, 2012; 13(5):336–349; doi: 10.1038/nrn3214
7.
CaseM, ShirinpourS, VijayakumarV, et al.Graph theory analysis reveals how sickle cell disease impacts neural networks of patients with more severe disease. Neuroimage Clin, 2019; 21:101599; doi: 10.1016/j.nicl.2018.11.009
8.
CaseM, ShirinpourS, ZhangH, et al.Increased theta band eeg power in sickle cell disease patients. J Pain Res, 2018; 11:67–76; doi: 10.2147/JPR.S145581
9.
CastroMMC, QuarantiniL, Batista-NevesS, et al.Visual analogue scales: Measurement of subjective phenomena. Rev Bras Anestesiol, 2006; 38:470–477.
FarmerMA, BalikiMN, ApkarianAV. A dynamic network perspective of chronic pain. Neurosci Lett, 2012; 520(2):197–203; doi: 10.1016/j.neulet.2012.05.001
12.
FerdekMA, OostermanJM, AdamczykAK, et al.Effective connectivity of beta oscillations in endometriosis-related chronic pain during rest and pain-related mental imagery. J Pain, 2019; 20(12):1446–1458; doi: 10.1016/j.jpain.2019.05.011
13.
FinnES, ShenX, ScheinostD, et al.Functional connectome fingerprinting: Identifying individuals using patterns of brain connectivity. Nat Neurosci, 2015; 18(11):1664–1671; doi: 10.1038/nn.4135
HirataY, AmigòJM, MatsuzakaY, et al.Detecting causality by combined use of multiple methods: Climate and brain examples. PLoS One, 2016; 11(7):e0158572; doi: 10.1371/journal.pone.0158572
16.
HumphriesM, GurneyK, PrescottT. The brainstem reticular formation is a small-world, not scale-free, network. Proceedings of the Royal Society. Proc Biol Sci, 2017; 273(1585):503–511; doi: 10.1098/rspb.2005.3354
17.
ItoCH, CampbellFQ, MontoyaP, et al.Patients with temporomandibular disorders and chronic pain of myofascial origin display reduced alpha power density and altered small-world properties of brain networks. Brain Imaging and Stimul, 2024; 3:e5648; doi: 10.1726/7/2965-3738bis.2024.e5648
18.
JiangJ-J, HuangZ-G, HuangL, et al.Directed dynamical influence is more detectable with noise. Sci Rep, 2016; 6:24088; doi: 10.1038/srep24088
19.
JooP, KimM, KishB, et al.Brain network hypersensitivity underlies pain crises in sickle cell disease. Sci Rep, 2024; 14(1):7315; doi: 10.1038/s41598-024-57473-5
20.
KitauraY, NishidaK, YoshimuraM, et al.Functional localization and effective connectivity of cortical theta and alpha oscillatory activity during an attention task. Clin Neurophysiol Pract, 2017; 2:193–200; doi: 10.1016/j.cnp.2017.09.002
21.
LiuJ, QinW, NanJ, et al.Gender-related differences in the dysfunctional resting networks of migraine suffers. PLoS One, 2011; 6(11):e27049; doi: 10.1371/journal.pone.0027049
22.
LiuJ, ZhaoL, LeiF, et al.Disrupted resting-state functional connectivity and its changing trend in migraine suffers. Hum Brain Mapp, 2015; 36(5):1892–1907; doi: 10.1002/hbm.22744
23.
LiuJ, ZhaoL, LiG, et al.Hierarchical alteration of brain structural and functional networks in female migraine sufferers. PLoS One, 2012; 7(12):e51250; doi: 10.1371/journal.pone.0051250
24.
LiuJ, ZhaoL, NanJ, et al.The trade-off between wiring cost and network topology in white matter structural networks in health and migraine. Exp Neurol, 2013; 248:196–204; doi: 10.1016/j.expneurol.2013.04.012
25.
LlinásRR, RibaryU, JeanmonodD, et al.Thalamocortical dysrhythmia: A neurological and neuropsychiatric syndrome characterized by magnetoencephalography. Proc Natl Acad Sci U S A, 1999; 96(26):15222–15227; doi: 10.1073/pnas.96.26.15222
26.
LlinásRR, UrbanoFJ, LeznikE, et al.Rhythmic and dysrhythmic thalamocortical dynamics: Gaba systems and the edge effect. Trends Neurosci, 2005; 28(6):325–333; doi: 10.1016/j.tins.2005.04.006
27.
LopesTS, SantanaJE, SilvaWS, et al.Increased delta and theta power density in sickle cell disease individuals with chronic pain secondary to hip osteonecrosis: A resting-state Eeg Study. Brain Topogr, 2024; 37(5):859–873; doi: 10.1007/s10548-023-01027-x
28.
LordL-D, StevnerAB, DecoG, et al.Understanding principles of integration and segregation using whole-brain computational connectomics: Implications for neuropsychiatric disorders. Philos Trans A Math Phys Eng Sci, 2017; 375(2096):20160283; doi: 10.1098/rsta.2016.0283
29.
MalouinF, RichardsCL, CarolL, et al.The kinesthetic and visual imagery questionnaire (KVIQ) for assessing motor imagery in persons with physical disabilities: A reliability and construct validity study. J Neurol Phys Ther, 2007; 31(1):20–29; doi: 10.1097/01.npt.0000260567.24122.64
30.
MansourA, BariaAT, TetreaultP, et al.Global disruption of degree rank order: A hallmark of chronic pain. Sci Rep, 2016; 6:34853; doi: 10.1038/srep34853
31.
MayES, NickelMM, Ta DinhS, et al.Prefrontal gamma oscillations reflect ongoing pain intensity in chronic back pain patients. Hum Brain Mapp, 2019; 40(1):293–305; doi: 10.1002/hbm.24373
32.
McBrideJC, ZhaoX, MunroNB, et al.Sugihara causality analysis of scalp eeg for detection of early alzheimer’s disease. Neuroimage Clin, 2015; 7:258–265; doi: 10.1016/j.nicl.2014.12.005
33.
McCrackenJM, WeigelRS. Convergent cross-mapping and pairwise asymmetric inference. Phys Rev E Stat Nonlin Soft Matter Phys, 2014; 90(6):062903; doi: 10.1103/PhysRevE.90.062903
34.
MenesesFM, QueirósFC, MontoyaP, et al.Patients with rheumatoid arthritis and chronic pain display enhanced alpha power density at rest. Front Hum Neurosci, 2016; 10:395; doi: 10.3389/fnhum.2016.00395
35.
MønsterD, FusaroliR, TylénK, et al.Causal inference from noisy time-series data—testing the convergent cross-mapping algorithm in the presence of noise and external influence. Future Generation Computer Systems, 2017; 73:52–62; doi: 10.1016/j.future.2016.12.009
36.
PinheiroESS, QueirósFC, MontoyaP, et al.Electroencephalographic patterns in chronic pain: A systematic review of the literature. PLoS One, 2016; 11(2):e0149085; doi: 10.1371/journal.pone.0149085
37.
RosárioRS, CardosoPT, MuñozMA, et al.Motif-synchronization: A new method for analysis of dynamic brain networks with eeg. Physica A: Statistical Mechanics and Its Applications, 2015; 439:7–19; doi: 10.1016/j.physa.2015.07.018
38.
RossellóF, MuñozMA, DuschekS, et al.Affective modulation of brain and autonomic responses in patients with fibromyalgia. Psychosom Med, 2015; 77(7):721–732; doi: 10.1097/PSY.0000000000000217
39.
SantanaJERS, BaptistaAF, LucenaR, et al.Altered dynamic brain connectivity in individuals with sickle cell disease and chronic pain secondary to hip osteonecrosis. Clin EEG Neurosci, 2023; 54(3):333–342; doi: 10.1177/15500594211054297
40.
SantosJ, BritoJ, AndradeD, et al.Translation to portuguese and validation of the douleur neuropathique 4 questionnaire. J Pain, 2010; 11(5):484–490; doi: 10.1016/j.jpain.2009.09.014
41.
SchieckeK, PesterB, PiperD, et al.Advanced nonlinear approach to quantify directed interactions within eeg activity of children with temporal lobe epilepsy in their time course. EPJ Nonlinear Biomed Phys, 2017; 5:3; doi: 10.1051/epjnbp/2017002
42.
SmitDJA, StamCJ, PosthumaD, et al.Heritability of “small-world” networks in the brain: A graph theoretical analysis of resting-state eeg functional connectivity. Hum Brain Mapp, 2008; 29(12):1368–1378; doi: 10.1002/hbm.20468
43.
SpornsO. Network attributes for segregation and integration in the human brain. Curr Opin Neurobiol, 2013; 23(2):162–171; doi: 10.1016/j.conb.2012.11.015
44.
SpornsO, ChialvoDR, KaiserM, et al.Organization, development and function of complex brain networks. Trends Cogn Sci, 2004; 8(9):418–425; doi: 10.1016/j.tics.2004.07.008
45.
StamCJ. Modern network science of neurological disorders. Nat Rev Neurosci, 2014; 15(10):683–695; doi: 10.1038/nrn3801
46.
SugiharaG, MayR, YeH, et al.Detecting causality in complex ecosystems. Science, 2012; 338(6106):496–500; doi: 10.1126/science.1227079
47.
Ta DinhS, NickelMM, TiemannL, et al.Brain dysfunction in chronic pain patients assessed by resting-state electroencephalography. Pain, 2019; 160(12):2751–2765; doi: 10.1097/j.pain.0000000000001666
48.
Van den HeuvelMP, StamCJ, BoersmaM, et al.Small-world and scale-free organization of voxel-based resting-state functional connectivity in the human brain. Neuroimage, 2008; 43(3):528–539; doi: 10.1016/j.neuroimage.2008.08.010
49.
VecchioF, Di IorioR, MiragliaF, et al.Transcranial direct current stimulation generates a transient increase of small-world in brain connectivity: An eeg graph theoretical analysis. Exp Brain Res, 2018; 236(4):1117–1127; doi: 10.1007/s00221-018-5200-z
50.
WangQ, PercM, DuanZ, et al.Impact of delays and rewiring on the dynamics of small-world neuronal networks with two types of coupling. Physica A: Statistical Mechanics and Its Applications, 2010; 389(16):3299–3306; doi: 10.1016/j.physa.2010.03.031
51.
WangJ, ZuoX, YongH. Graph-based network analysis of resting-state functional MRI. Front Syst Neurosci, 2010; 4:16; doi: 10.3389/fnsys.2010.00016
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