Reduced resilience of brain state transitions in anti-N-methyl-D-aspartate receptor encephalitis.
autoimmune encephalitis
functional brain states
functional connectivity dynamics
graph analysis
transition trajectories
Journal
The European journal of neuroscience
ISSN: 1460-9568
Titre abrégé: Eur J Neurosci
Pays: France
ID NLM: 8918110
Informations de publication
Date de publication:
02 2023
02 2023
Historique:
revised:
28
11
2022
received:
05
08
2022
accepted:
29
11
2022
pubmed:
15
12
2022
medline:
4
2
2023
entrez:
14
12
2022
Statut:
ppublish
Résumé
Patients with anti-N-methyl-aspartate receptor (NMDA) receptor encephalitis suffer from a severe neuropsychiatric syndrome, yet most patients show no abnormalities in routine magnetic resonance imaging. In contrast, advanced neuroimaging studies have consistently identified disrupted functional connectivity in these patients, with recent work suggesting increased volatility of functional state dynamics. Here, we investigate these network dynamics through the spatiotemporal trajectory of meta-state transitions, yielding a time-resolved account of brain state exploration in anti-NMDA receptor encephalitis. To this end, resting-state functional magnetic resonance imaging data were acquired in 73 patients with anti-NMDA receptor encephalitis and 73 age- and sex-matched healthy controls. Time-resolved functional connectivity was clustered into brain meta-states, giving rise to a time-resolved transition network graph with states as nodes and transitions between brain meta-states as weighted, directed edges. Network topology, robustness and transition cost of these transition networks were compared between groups. Transition networks of patients showed significantly lower local efficiency (t = -2.41, p
Substances chimiques
Receptors, N-Methyl-D-Aspartate
0
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
568-579Informations de copyright
© 2022 The Authors. European Journal of Neuroscience published by Federation of European Neuroscience Societies and John Wiley & Sons Ltd.
Références
Achard, S. (2006). A resilient, low-frequency, small-world human brain functional network with highly connected association cortical hubs. Journal of Neuroscience, 26(1), 63-72. https://doi.org/10.1523/JNEUROSCI.3874-05.2006
Aerts, H., Fias, W., Caeyenberghs, K., & Marinazzo, D. (2016). Brain networks under attack: Robustness properties and the impact of lesions. Brain, 139(12), 3063-3083. https://doi.org/10.1093/brain/aww194
Allen, E. A., Damaraju, E., Plis, S. M., Erhardt, E. B., Eichele, T., & Calhoun, V. D. (2014). Tracking whole-brain connectivity dynamics in the resting state. Cerebral Cortex (New York, N.Y.: 1991), 24(3), 663-676. https://doi.org/10.1093/cercor/bhs352
Bassett, D. S., Wymbs, N. F., Porter, M. A., Mucha, P. J., Carlson, J. M., & Grafton, S. T. (2011). Dynamic reconfiguration of human brain networks during learning. Proceedings of the National Academy of Sciences, 108(18), 7641-7646. https://doi.org/10.1073/pnas.1018985108
Benjamini, Y., & Hochberg, Y. (1995). Controlling the false discovery rate: A practical and powerful approach to multiple testing. Journal of the Royal Statistical Society: Series B (Methodological), 57(1), 289-300. https://doi.org/10.1111/j.2517-6161.1995.tb02031.x
Bertolero, M. A., Yeo, B. T. T., & D'Esposito, M. (2015). The modular and integrative functional architecture of the human brain. Proceedings of the National Academy of Sciences, 112(49), E6798-E6807. https://doi.org/10.1073/pnas.1510619112
Biswal, B., Zerrin Yetkin, F., Haughton, V. M., & Hyde, J. S. (1995). Functional connectivity in the motor cortex of resting human brain using echo-planar mri. Magnetic Resonance in Medicine, 34(4), 537-541. https://doi.org/10.1002/mrm.1910340409
Cai, L., Liang, Y., Huang, H., Zhou, X., & Zheng, J. (2020). Cerebral functional activity and connectivity changes in anti-N-methyl-D-aspartate receptor encephalitis: A resting-state fMRI study. NeuroImage: Clinical, 25, 102189. https://doi.org/10.1016/j.nicl.2020.102189
Calhoun, V. D., Miller, R., Pearlson, G., & Adalı, T. (2014). The Chronnectome: Time-varying connectivity networks as the next frontier in fMRI data discovery. Neuron, 84(2), 262-274. https://doi.org/10.1016/j.neuron.2014.10.015
Chang, C., & Glover, G. H. (2010). Time-frequency dynamics of resting-state brain connectivity measured with fMRI. NeuroImage, 50(1), 81-98. https://doi.org/10.1016/j.neuroimage.2009.12.011
Chen, Z., Zhou, J., Wu, D., Ji, C., Luo, B., & Wang, K. (2022). Altered executive control network connectivity in anti-NMDA receptor encephalitis. Annals of Clinical and Translational Neurology, 9(1), 30-40. https://doi.org/10.1002/acn3.51487
Crossley, N. A., Mechelli, A., Vertes, P. E., Winton-Brown, T. T., Patel, A. X., Ginestet, C. E., McGuire, P., & Bullmore, E. T. (2013). Cognitive relevance of the community structure of the human brain functional coactivation network. Proceedings of the National Academy of Sciences, 110(28), 11583-11588. https://doi.org/10.1073/pnas.1220826110
Dalmau, J., Armangué, T., Planagumà, J., Radosevic, M., Mannara, F., Leypoldt, F., Geis, C., Lancaster, E., Titulaer, M. J., Rosenfeld, M. R., & Graus, F. (2019). An update on anti-NMDA receptor encephalitis for neurologists and psychiatrists: Mechanisms and models. The Lancet Neurology, 18(11), 1045-1057. https://doi.org/10.1016/S1474-4422(19)30244-3
Dalmau, J., Lancaster, E., Martinez-Hernandez, E., Rosenfeld, M. R., & Balice-Gordon, R. (2011). Clinical experience and laboratory investigations in patients with anti-NMDAR encephalitis. The Lancet Neurology, 10(1), 63-74. https://doi.org/10.1016/S1474-4422(10)70253-2
de Vico Fallani, F., Aparecido, R. F., da Fontoura, C. L., Mattia, D., Cincotti, F., Astolfi, L., Vecchiato, G., Tabarrini, A., Salinari, S., & Babiloni, F. (2009). Analysis of the connection redundancy in functional networks from high-resolution EEG: A preliminary study. Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2009, 2204-2207. https://doi.org/10.1109/IEMBS.2009.5334882
Deco, G., Jirsa, V. K., & McIntosh, A. R. (2011). Emerging concepts for the dynamical organization of resting-state activity in the brain. Nature Reviews Neuroscience, 12(1), 43-56. https://doi.org/10.1038/nrn2961
Deco, G., Kringelbach, M. L., Jirsa, V. K., & Ritter, P. (2017). The dynamics of resting fluctuations in the brain: Metastability and its dynamical cortical core. Scientific Reports, 7(1), 3095. https://doi.org/10.1038/s41598-017-03073-5
Eijlers, A. J. C., Wink, A. M., Meijer, K. A., Douw, L., Geurts, J. J. G., & Schoonheim, M. M. (2019). Reduced network dynamics on functional MRI signals cognitive impairment in multiple sclerosis. Radiology, 292(2), 449-457. https://doi.org/10.1148/radiol.2019182623
Finke, C., Kopp, U. A., Prüss, H., Dalmau, J., Wandinger, K.-P., & Ploner, C. J. (2012). Cognitive deficits following anti-NMDA receptor encephalitis. Journal of Neurology, Neurosurgery & Psychiatry, 83(2), 195-198. https://doi.org/10.1136/jnnp-2011-300411
Finke, C., Kopp, U. A., Scheel, M., Pech, L.-M., Soemmer, C., Schlichting, J., Leypoldt, F., Brandt, A. U., Wuerfel, J., Probst, C., Ploner, C. J., Prüss, H., & Paul, F. (2013). Functional and structural brain changes in anti-N-methyl-D-aspartate receptor encephalitis. Annals of Neurology, 74(2), 284-296. https://doi.org/10.1002/ana.23932
Gibson, L. L., McKeever, A., Coutinho, E., Finke, C., & Pollak, T. A. (2020). Cognitive impact of neuronal antibodies: Encephalitis and beyond. Translational Psychiatry, 10(1), 304. https://doi.org/10.1038/s41398-020-00989-x
Gibson, L. L., Pollak, T. A., Blackman, G., Thornton, M., Moran, N., & David, A. S. (2019). The psychiatric phenotype of anti-NMDA receptor encephalitis. The Journal of Neuropsychiatry and Clinical Neurosciences, 31(1), 70-79. https://doi.org/10.1176/appi.neuropsych.17120343
Graus, F., Titulaer, M. J., Balu, R., Benseler, S., Bien, C. G., Cellucci, T., Cortese, I., Dale, R. C., Gelfand, J. M., Geschwind, M., Glaser, C. A., Honnorat, J., Höftberger, R., Iizuka, T., Irani, S. R., Lancaster, E., Leypoldt, F., Prüss, H., Rae-Grant, A., … Dalmau, J. (2016). A clinical approach to diagnosis of autoimmune encephalitis. The Lancet. Neurology, 15(4), 391-404. https://doi.org/10.1016/S1474-4422(15)00401-9
Gu, S., Betzel, R. F., Mattar, M. G., Cieslak, M., Delio, P. R., Grafton, S. T., Pasqualetti, F., & Bassett, D. S. (2017). Optimal trajectories of brain state transitions. NeuroImage, 148, 305-317. https://doi.org/10.1016/j.neuroimage.2017.01.003
Heine, J., Kopp, U. A., Klag, J., Ploner, C. J., Prüss, H., & Finke, C. (2021). Long-term cognitive outcome in anti-N-methyl-D-aspartate receptor encephalitis. Annals of Neurology, 90(6), 949-961. https://doi.org/10.1002/ana.26241
Heine, J., Prüss, H., Bartsch, T., Ploner, C. J., Paul, F., & Finke, C. (2015). Imaging of autoimmune encephalitis-Relevance for clinical practice and hippocampal function. Neuroscience, 309, 68-83. https://doi.org/10.1016/j.neuroscience.2015.05.037
Kringelbach, M. L., & Deco, G. (2020). Brain states and transitions: Insights from computational neuroscience. Cell Reports, 32(10), 108128. https://doi.org/10.1016/j.celrep.2020.108128
Krohn, S., von Schwanenflug, N., Waschke, L., Romanello, A., Gell, M., Garrett, D. D., & Finke, C. (2021). A spatiotemporal complexity architecture of human brain activity [preprint]. bioRxiv. https://doi.org/10.1101/2021.06.04.446948
Latora, V., & Marchiori, M. (2001). Efficient behavior of small-world networks. Physical Review Letters, 87(19), 198701. https://doi.org/10.1103/PhysRevLett.87.198701
Loh, M., Rolls, E. T., & Deco, G. (2007). A dynamical systems hypothesis of schizophrenia. PLoS Computational Biology, 3(11), e228. https://doi.org/10.1371/journal.pcbi.0030228
Lord, L.-D., Expert, P., Atasoy, S., Roseman, L., Rapuano, K., Lambiotte, R., Nutt, D. J., Deco, G., Carhart-Harris, R. L., Kringelbach, M. L., & Cabral, J. (2019). Dynamical exploration of the repertoire of brain networks at rest is modulated by psilocybin. NeuroImage, 199, 127-142. https://doi.org/10.1016/j.neuroimage.2019.05.060
Lord, L.-D., Expert, P., Huckins, J. F., & Turkheimer, F. E. (2013). Cerebral energy metabolism and the brain's functional network architecture: An integrative review. Journal of Cerebral Blood Flow & Metabolism, 33(9), 1347-1354. https://doi.org/10.1038/jcbfm.2013.94
Lynall, M.-E., Bassett, D. S., Kerwin, R., McKenna, P. J., Kitzbichler, M., Muller, U., & Bullmore, E. (2010). Functional connectivity and brain networks in schizophrenia. Journal of Neuroscience, 30(28), 9477-9487. https://doi.org/10.1523/JNEUROSCI.0333-10.2010
Parkes, L., Fulcher, B., Yücel, M., & Fornito, A. (2018). An evaluation of the efficacy, reliability, and sensitivity of motion correction strategies for resting-state functional MRI. NeuroImage, 171, 415-436. https://doi.org/10.1016/j.neuroimage.2017.12.073
Peer, M., Prüss, H., Ben-Dayan, I., Paul, F., Arzy, S., & Finke, C. (2017). Functional connectivity of large-scale brain networks in patients with anti-NMDA receptor encephalitis: An observational study. The Lancet. Psychiatry, 4(10), 768-774. https://doi.org/10.1016/S2215-0366(17)30330-9
Power, J. D., Mitra, A., Laumann, T. O., Snyder, A. Z., Schlaggar, B. L., & Petersen, S. E. (2014). Methods to detect, characterize, and remove motion artifact in resting state fMRI. NeuroImage, 84, 320-341. https://doi.org/10.1016/j.neuroimage.2013.08.048
Pruim, R. H. R., Mennes, M., van Rooij, D., Llera, A., Buitelaar, J. K., & Beckmann, C. F. (2015). ICA-AROMA: A robust ICA-based strategy for removing motion artifacts from fMRI data. NeuroImage, 112, 267-277. https://doi.org/10.1016/j.neuroimage.2015.02.064
Ramirez-Mahaluf, J. P., Medel, V., Tepper, Á., Alliende, L. M., Sato, J. R., Ossandon, T., & Crossley, N. A. (2020). Transitions between human functional brain networks reveal complex, cost-efficient and behaviorally-relevant temporal paths. NeuroImage, 219, 117027. https://doi.org/10.1016/j.neuroimage.2020.117027
Rolls, E. T. (2012). Glutamate, obsessive-compulsive disorder, schizophrenia, and the stability of cortical attractor neuronal networks. Pharmacology Biochemistry and Behavior, 100(4), 736-751. https://doi.org/10.1016/j.pbb.2011.06.017
Rolls, E. T. (2021). Attractor cortical neurodynamics, schizophrenia, and depression. Translational Psychiatry, 11(1), 215. https://doi.org/10.1038/s41398-021-01333-7
Shine, J. M., Koyejo, O., Bell, P. T., Gorgolewski, K. J., Gilat, M., & Poldrack, R. A. (2015). Estimation of dynamic functional connectivity using multiplication of temporal derivatives. NeuroImage, 122, 399-407. https://doi.org/10.1016/j.neuroimage.2015.07.064
Sporns, O., & Betzel, R. F. (2016). Modular brain networks. Annual Review of Psychology, 67(1), 613-640. https://doi.org/10.1146/annurev-psych-122414-033634
Tognoli, E., & Kelso, J. A. S. (2014). The metastable brain. Neuron, 81(1), 35-48. https://doi.org/10.1016/j.neuron.2013.12.022
von Schwanenflug, N., Krohn, S., Heine, J., Paul, F., Prüss, H., & Finke, C. (2022). State-dependent signatures of anti-N-methyl-d-aspartate receptor encephalitis. Brain Communications, 4(1), fcab298. https://doi.org/10.1093/braincomms/fcab298
Yeo, B. T., Krienen, F. M., Sepulcre, J., Sabuncu, M. R., Lashkari, D., Hollinshead, M., Roffman, J. L., Smoller, J. W., Zöllei, L., Polimeni, J. R., Fischl, B., Liu, H., & Buckner, R. L. (2011). The organization of the human cerebral cortex estimated by intrinsic functional connectivity. Journal of Neurophysiology, 106(3), 1125-1165. https://doi.org/10.1152/jn.00338.2011