Whole brain polarity regime dynamics are significantly disrupted in schizophrenia and correlate strongly with network connectivity measures
Autoři:
Robyn L. Miller aff001; Godfrey Pearlson aff003; Vince D. Calhoun aff001
Působiště autorů:
Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): Georgia State Univsersity, Georgia Institute of Technology, Emory University, Atlanta, GA, United States of America
aff001; Georgia State University, Atlanta, GA, United States of America
aff002; Yale University School of Medicine, New Haven, CT, United States of America
aff003
Vyšlo v časopise:
PLoS ONE 14(12)
Kategorie:
Research Article
doi:
https://doi.org/10.1371/journal.pone.0224744
Souhrn
From a large clinical blood oxygenation level dependent (BOLD) functional magnetic resonance imaging (fMRI) study, we report several interrelated findings involving transient supra-network brainwide states characterized by a saturation phenomenon we are referring to as “polarization.” These are whole-brain states in which the voxelwise-normalized BOLD (vn-BOLD) activation of a large proportion of voxels is simultaneously either very high or very low. The presence of such states during a resting-state fMRI (rs-fMRI) scan is significantly anti-correlated with diagnosed schizophrenia, significantly anti-correlated with connectivity between subcortical networks and auditory, visual and sensorimotor networks and also significantly anti-correlated with contemporaneous occupancy of transient functional network connectivity states featuring broad disconnectivity or strong inhibitory connections between the default mode and other networks. Conversely, the presence of highly polarized vn-BOLD states is significantly correlated with connectivity strength between auditory, visual and sensorimotor networks and with contemporaneous occupancy of transient whole-brain patterns of strongly modularized network connectivity and diffuse hyperconnectivity. Despite their consistency with well-documented effects of schizophrenia on static and time-varying functional network connectivity, the observed relationships between polarization and network connectivity are with very few exceptions unmediated by schizophrenia diagnosis. Many differences observed between patients and controls are echoed within the patient population itself in the effect patterns of positive symptomology (e.g. hallucinations, delusions, grandiosity). Our findings highlight a particular whole-brain spatiotemporal BOLD activation phenomenon that differs markedly between healthy subjects and schizophrenia patients, one that also strongly informs time-resolved network connectivity patterns that are associated with this serious clinical disorder.
Klíčová slova:
Functional magnetic resonance imaging – k means clustering – Neural networks – Patients – Schizophrenia – Vision – Hallucinations – Convolutional coding
Zdroje
1. Abrol A, Damaraju E, Miller RL, Stephen JM, Claus ED, Mayer AR, et al. Replicability of time-varying connectivity patterns in large resting state fMRI samples. Neuroimage. 2017;163:160–76. doi: 10.1016/j.neuroimage.2017.09.020 WOS:000418641800014. 28916181
2. Allen E, Damaraju E, Plis SM, Erhardt E, Eichele T, Calhoun VD. Tracking whole-brain connectivity dynamics in the resting state. Cereb Cortex. 2014;24(3):663–76. PubMed Central PMCID: PMC3920766. doi: 10.1093/cercor/bhs352 23146964
3. Calhoun VD, Miller R, Pearlson G, Adali T. The chronnectome: time-varying connectivity networks as the next frontier in fMRI data discovery. Neuron. 2014;84(2):262–74. doi: 10.1016/j.neuron.2014.10.015 25374354; PubMed Central PMCID: PMC4372723.
4. Damaraju E, Allen EA, Belger A, Ford J, McEwen SC, Mathalon D, et al. Dynamic functional connectivity analysis reveals transient states of dysconnectivity in schizophrenia. Neuroimage: Clinical. 2014;5:298–308. doi: 10.1016/j.nicl.2014.07.003 25161896; PubMed Central PMCID: PMC4141977.
5. Hindriks R, Adhikari MH, Murayama Y, Ganzetti M, Mantini D, Logothetis NK, et al. Can sliding-window correlations reveal dynamic functional connectivity in resting-state fMRI? Neuroimage. 2016;127:242–56. doi: 10.1016/j.neuroimage.2015.11.055 26631813; PubMed Central PMCID: PMC4758830.
6. Hutchison RM, Womelsdorf T, Allen EA, Bandettini P, Calhoun VD, Corbetta M, et al. Dynamic functional connectivity: promises, issues, and interpretations. NeuroImage. 2013;80:360–78. PubMed Central PMCID: PMC3807588. doi: 10.1016/j.neuroimage.2013.05.079 23707587
7. Laumann TO, Snyder AZ, Mitra A, Gordon EM, Gratton C, Adeyemo B, et al. On the Stability of BOLD fMRI Correlations. Cereb Cortex. 2016. doi: 10.1093/cercor/bhw265 27591147.
8. Miller RL, Abrol A, Adali T, Levin-Schwarz Y, Calhoun VD. Resting-State fMRI Dynamics and Null Models: Perspectives, Sampling Variability, and Simulations. Frontiers in Neuroscience. 2018;12. Artn 551 doi: 10.3389/Fnins.2018.00551 WOS:000443842600001. 30237758
9. Preti MG, Bolton TA, Van De Ville D. The dynamic functional connectome: State-of-the-art and perspectives. Neuroimage. 2016. doi: 10.1016/j.neuroimage.2016.12.061 28034766.
10. Tagliazucchi E, Laufs H. Multimodal imaging of dynamic functional connectivity. Front Neurol. 2015;6:10. doi: 10.3389/fneur.2015.00010 25762977; PubMed Central PMCID: PMC4329798.
11. Xie H, Calhoun VD, Gonzalez-Castillo J, Damaraju E, Miller R, Bandettini PA, et al. Whole-brain connectivity dynamics reflect both task-specific and individual-specific modulation: A multitask study. Neuroimage. 2018;180:495–504. doi: 10.1016/j.neuroimage.2017.05.050 WOS:000443271100014. 28549798
12. Zalesky A, Breakspear M. Towards a statistical test for functional connectivity dynamics. Neuroimage. 2015;114:466–70. doi: 10.1016/j.neuroimage.2015.03.047 25818688.
13. Keilholz S, Caballero-Gaudes C, Bandettini P, Deco G, Calhoun V. Time-Resolved Resting-State Functional Magnetic Resonance Imaging Analysis: Current Status, Challenges, and New Directions. Brain Connect. 2017;7(8):465–81. doi: 10.1089/brain.2017.0543 28874061; PubMed Central PMCID: PMC5653134.
14. Iraji A, Deramus TP, Lewis N, Yaesoubi M, Stephen JM, Erhardt E, et al. The spatial chronnectome reveals a dynamic interplay between functional segregation and integration. Human brain mapping. 2019;40(10):3058–77. Epub 2019/03/19. doi: 10.1002/hbm.24580 30884018; PubMed Central PMCID: PMC6548674.
15. Iraji A, Fu Z, Damaraju E, DeRamus TP, Lewis N, Bustillo JR, et al. Spatial dynamics within and between brain functional domains: A hierarchical approach to study time-varying brain function. Human brain mapping. 2019;40(6):1969–86. Epub 2018/12/28. doi: 10.1002/hbm.24505 30588687; PubMed Central PMCID: PMC6692083.
16. Miller RL, Calhoun VD. Schizophrenia Signicantly Impacts Network Spatial Map Dynamics and Spatial Network Connectivity. 25th Annual Meeting of the Organization for Human Brain Mapping Rome Italy 2019.
17. Miller RL, Erhardt EB, Agcaoglu O, Allen EA, Michael AM, Turner JA, et al. Multidimensional frequency domain analysis of full-volume fMRI reveals significant effects of age, gender, and mental illness on the spatiotemporal organization of resting-state brain activity. Frontiers in neuroscience. 2015;9.
18. Gu S, Cieslak M, Baird B, Muldoon SF, Grafton ST, Pasqualetti F, et al. The Energy Landscape of Neurophysiological Activity Implicit in Brain Network Structure. Sci Rep-Uk. 2018;8. ARTN 2507 doi: 10.1038/s41598-018-20123-8 WOS:000424189400023. 29410486
19. Liu X, Zhang N, Chang C, Duyn JH. Co-activation patterns in resting-state fMRI signals. Neuroimage. 2018;180(Pt B):485–94. doi: 10.1016/j.neuroimage.2018.01.041 29355767; PubMed Central PMCID: PMC6082734.
20. Wohlberg B. Efficient Convolutional Sparse Coding. 2014 Ieee International Conference on Acoustics, Speech and Signal Processing (Icassp). 2014. WOS:000343655307042.
21. Kay SR, Opler LA, Lindenmayer J-P. The Positive and Negative Syndrome Scale (PANSS): rationale and standardisation. The British Journal of Psychiatry. 1989.
22. Damaraju E, Allen EA, Belger A, Ford JM, McEwen S, Mathalon DH, et al. Dynamic functional connectivity analysis reveals transient states of dysconnectivity in schizophrenia. NeuroImage: Clinical. 2014;5:298–308. doi: 10.1016/j.nicl.2014.07.003 PMC4141977. 25161896
23. Miller RL, Yaesoubi M, Turner JA, Mathalon D, Preda A, Pearlson G, et al. Higher Dimensional Meta-State Analysis Reveals Reduced Resting fMRI Connectivity Dynamism in Schizophrenia Patients. Plos One. 2016;11(3). ARTN e0149849 doi: 10.1371/journal.pone.0149849 WOS:000372574900021. 26981625
24. Liu Y, Gao JH, Liu HL, Fox PT. The temporal response of the brain after eating revealed by functional MRI. Nature. 2000;405(6790):1058–62. Epub 2000/07/13. doi: 10.1038/35016590 10890447.
25. Morgan VL, Li Y, Abou-Khalil B, Gore JC. Development of 2dTCA for the detection of irregular, transient BOLD activity. Human brain mapping. 2008;29(1):57–69. Epub 2007/02/10. doi: 10.1002/hbm.20362 17290367; PubMed Central PMCID: PMC2719759.
26. Pittman-Polletta BR, Kocsis B, Vijayan S, Whittington MA, Kopell NJ. Brain rhythms connect impaired inhibition to altered cognition in schizophrenia. Biol Psychiatry. 2015;77(12):1020–30. Epub 2015/04/09. doi: 10.1016/j.biopsych.2015.02.005 25850619; PubMed Central PMCID: PMC4444389.
27. Shaw AD, Knight L, Freeman TCA, Williams GM, Moran RJ, Friston KJ, et al. Oscillatory, Computational, and Behavioral Evidence for Impaired GABAergic Inhibition in Schizophrenia. Schizophr Bull. 2019. Epub 2019/06/21. doi: 10.1093/schbul/sbz066 31219602.
28. Brennan AM, Harris AW, Williams LM. Functional dysconnectivity in schizophrenia and its relationship to neural synchrony. Expert Rev Neurother. 2013;13(7):755–65. Epub 2013/08/01. doi: 10.1586/14737175.2013.811899 23898848.
29. Pettersson-Yeo W, Allen P, Benetti S, McGuire P, Mechelli A. Dysconnectivity in schizophrenia: where are we now? Neurosci Biobehav Rev. 2011;35(5):1110–24. Epub 2010/12/01. doi: 10.1016/j.neubiorev.2010.11.004 21115039.
30. Uhlhaas PJ. Dysconnectivity, large-scale networks and neuronal dynamics in schizophrenia. Curr Opin Neurobiol. 2013;23(2):283–90. Epub 2012/12/12. doi: 10.1016/j.conb.2012.11.004 23228430.
31. Bansal S, Robinson BM, Leonard CJ, Hahn B, Luck SJ, Gold JM. Failures in Top-Down Control in Schizophrenia Revealed by Patterns of Saccadic Eye Movements. J Abnorm Psychol. 2019;128(5):415–22. doi: 10.1037/abn0000442 WOS:000474235800005. 31192637
32. Keshavan MS, Anderson S, Pettegrew JW. Is Schizophrenia Due to Excessive Synaptic Pruning in the Prefrontal Cortex—the Feinberg Hypothesis Revisited. J Psychiatr Res. 1994;28(3):239–65. doi: 10.1016/0022-3956(94)90009-4 WOS:A1994NW08500005. 7932285
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PLOS One
2019 Číslo 12
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