Disconnectome of the migraine brain: a "connectopathy" model.
Advanced neuroimaging
Brain network
Connectome
Graph analysis
Migraine
Journal
The journal of headache and pain
ISSN: 1129-2377
Titre abrégé: J Headache Pain
Pays: England
ID NLM: 100940562
Informations de publication
Date de publication:
28 Aug 2021
28 Aug 2021
Historique:
received:
30
06
2021
accepted:
10
08
2021
entrez:
29
8
2021
pubmed:
30
8
2021
medline:
1
9
2021
Statut:
epublish
Résumé
In the past decades a plethora of studies has been conducted to explore resting-state functional connectivity (RS-FC) of the brain networks in migraine with conflicting results probably due to the variability and susceptibility of signal fluctuations across the course of RS-FC scan. On the other hand, the structural substrates enabling the functional communications among the brain connectome, characterized by higher stability and reproducibility, have not been widely investigated in migraine by means of graph analysis approach. We hypothesize a rearrangement of the brain connectome with an increase of both strength and density of connections between cortical areas specifically involved in pain perception, processing and modulation in migraine patients. Moreover, such connectome rearrangement, inducing an imbalance between the competing parameters of network efficiency and segregation, may underpin a mismatch between energy resources and demand representing the neuronal correlate of the energetically dysfunctional migraine brain. We investigated, using diffusion-weighted MRI imaging tractography-based graph analysis, the graph-topological indices of the brain "connectome", a set of grey matter regions (nodes) structurally connected by white matter paths (edges) in 94 patients with migraine without aura compared to 91 healthy controls. We observed in migraine patients compared to healthy controls: i) higher local and global network efficiency (p < 0.001) and ii) higher local and global clustering coefficient (p < 0.001). Moreover, we found changes in the hubs topology in migraine patients with: i) posterior cingulate cortex and inferior parietal lobule (encompassing the so-called neurolimbic-pain network) assuming the hub role and ii) fronto-orbital cortex, involved in emotional aspects, and visual areas, involved in migraine pathophysiology, losing the hub role. Finally, we found higher connection (edges) probability between cortical nodes involved in pain perception and modulation as well as in cognitive and affective attribution of pain experiences, in migraine patients when compared to healthy controls (p < 0.001). No correlations were found between imaging and clinical parameters of disease severity. The imbalance between the need of investing resources to promote network efficiency and the need of minimizing the metabolic cost of wiring probably represents the mechanism underlying migraine patients' susceptibility to triggers. Such changes in connectome topography suggest an intriguing pathophysiological model of migraine as brain "connectopathy".
Sections du résumé
BACKGROUND
BACKGROUND
In the past decades a plethora of studies has been conducted to explore resting-state functional connectivity (RS-FC) of the brain networks in migraine with conflicting results probably due to the variability and susceptibility of signal fluctuations across the course of RS-FC scan. On the other hand, the structural substrates enabling the functional communications among the brain connectome, characterized by higher stability and reproducibility, have not been widely investigated in migraine by means of graph analysis approach. We hypothesize a rearrangement of the brain connectome with an increase of both strength and density of connections between cortical areas specifically involved in pain perception, processing and modulation in migraine patients. Moreover, such connectome rearrangement, inducing an imbalance between the competing parameters of network efficiency and segregation, may underpin a mismatch between energy resources and demand representing the neuronal correlate of the energetically dysfunctional migraine brain.
METHODS
METHODS
We investigated, using diffusion-weighted MRI imaging tractography-based graph analysis, the graph-topological indices of the brain "connectome", a set of grey matter regions (nodes) structurally connected by white matter paths (edges) in 94 patients with migraine without aura compared to 91 healthy controls.
RESULTS
RESULTS
We observed in migraine patients compared to healthy controls: i) higher local and global network efficiency (p < 0.001) and ii) higher local and global clustering coefficient (p < 0.001). Moreover, we found changes in the hubs topology in migraine patients with: i) posterior cingulate cortex and inferior parietal lobule (encompassing the so-called neurolimbic-pain network) assuming the hub role and ii) fronto-orbital cortex, involved in emotional aspects, and visual areas, involved in migraine pathophysiology, losing the hub role. Finally, we found higher connection (edges) probability between cortical nodes involved in pain perception and modulation as well as in cognitive and affective attribution of pain experiences, in migraine patients when compared to healthy controls (p < 0.001). No correlations were found between imaging and clinical parameters of disease severity.
CONCLUSION
CONCLUSIONS
The imbalance between the need of investing resources to promote network efficiency and the need of minimizing the metabolic cost of wiring probably represents the mechanism underlying migraine patients' susceptibility to triggers. Such changes in connectome topography suggest an intriguing pathophysiological model of migraine as brain "connectopathy".
Identifiants
pubmed: 34454429
doi: 10.1186/s10194-021-01315-6
pii: 10.1186/s10194-021-01315-6
pmc: PMC8400754
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
102Informations de copyright
© 2021. The Author(s).
Références
Behav Neurol. 2019 May 5;2019:5808610
pubmed: 31191737
Neurology. 2020 Feb 18;94(7):e741-e751
pubmed: 31964691
Neural Plast. 2016;2016:9849087
pubmed: 26819781
Brain Imaging Behav. 2017 Apr;11(2):526-540
pubmed: 26922054
Nat Rev Neurosci. 2009 Mar;10(3):186-98
pubmed: 19190637
Magn Reson Med. 2003 Nov;50(5):1077-88
pubmed: 14587019
Neuron. 2012 Jan 26;73(2):219-34
pubmed: 22284178
Cephalalgia. 2016 Feb;36(2):139-47
pubmed: 25926619
Brain. 2014 Aug;137(Pt 8):2382-95
pubmed: 25057133
Sci Rep. 2015 Jun 25;5:10924
pubmed: 26109334
J Headache Pain. 2019 May 3;20(1):46
pubmed: 31053057
Cephalalgia. 2018 Jan;38(1):1-211
pubmed: 29368949
Headache. 2012 Nov-Dec;52(10):1553-65
pubmed: 22757613
PLoS One. 2013 Jun 10;8(6):e65511
pubmed: 23935801
Clin Neurophysiol Pract. 2017 Oct 24;2:206-213
pubmed: 30214997
Nat Hum Behav. 2019 Sep;3(9):988-998
pubmed: 31384023
Neuroimage. 2002 Jan;15(1):273-89
pubmed: 11771995
Cephalalgia. 2020 Apr;40(4):367-383
pubmed: 31674222
Magn Reson Med. 2010 Aug;64(2):554-66
pubmed: 20535807
Brain Connect. 2011;1(4):295-308
pubmed: 22432419
Cephalalgia. 2003 Nov;23(9):931; author reply 931
pubmed: 14616937
Neuroimage. 2008 Sep 1;42(3):1178-84
pubmed: 18598773
J Headache Pain. 2017 Sep 29;18(1):98
pubmed: 28963615
Proc Natl Acad Sci U S A. 2014 Jan 14;111(2):823-8
pubmed: 24297904
Headache. 2010 Feb;50(2):273-89
pubmed: 20039962
Cereb Cortex. 2021 May 10;31(6):3021-3033
pubmed: 33471126
Trends Cogn Sci. 2016 May;20(5):345-361
pubmed: 27026480
Nat Rev Neurol. 2019 Nov;15(11):627-643
pubmed: 31586135
Neuroimage. 2007 Jan 1;34(1):144-55
pubmed: 17070705
Hum Brain Mapp. 2015 May;36(5):1995-2013
pubmed: 25641208
Exp Neurol. 2013 Oct;248:196-204
pubmed: 23648629
J Headache Pain. 2019 Jan 9;20(1):3
pubmed: 30626318
Curr Neurol Neurosci Rep. 2017 Oct 23;17(12):95
pubmed: 29063211
Neuroscientist. 2011 Oct;17(5):575-91
pubmed: 21527724
Neuroimage. 2010 Sep;52(3):1059-69
pubmed: 19819337
Hum Brain Mapp. 2017 Oct;38(10):5250-5259
pubmed: 28731567
Proc Natl Acad Sci U S A. 2007 Jun 12;104(24):10240-5
pubmed: 17548818
Front Neuroanat. 2016 Mar 31;10:25
pubmed: 27064378
Neuroimage. 2004;23 Suppl 1:S208-19
pubmed: 15501092
Front Hum Neurosci. 2020 Jul 21;14:244
pubmed: 32792927
PLoS Comput Biol. 2009 May;5(5):e1000381
pubmed: 19412534
PLoS One. 2012;7(12):e51250
pubmed: 23227257
PLoS One. 2013 Oct 31;8(10):e77455
pubmed: 24204833
Nat Rev Neurosci. 2012 Apr 13;13(5):336-49
pubmed: 22498897
Cereb Cortex. 2009 Mar;19(3):524-36
pubmed: 18567609
Cogn Affect Behav Neurosci. 2002 Sep;2(3):264-70
pubmed: 12775190
Nat Rev Neurosci. 2019 Jul;20(7):435-446
pubmed: 31127193