Functional interactions in patients with hemianopia: A graph theory-based connectivity study of resting fMRI signal
Autoři:
Caterina A. Pedersini aff001; Joan Guàrdia-Olmos aff002; Marc Montalà-Flaquer aff003; Nicolò Cardobi aff001; Javier Sanchez-Lopez aff001; Giorgia Parisi aff001; Silvia Savazzi aff001; Carlo A. Marzi aff001
Působiště autorů:
Physiology and Psychology Section, Department of Neuroscience, Biomedicine and Movement Sciences, University of Verona, Verona, Italy
aff001; Department of Social Psychology and Quantitative Psychology, School of Psychology, Institute of Neuroscience, Institute of Complex Systems, University of Barcelona, Barcelona, Spain
aff002; Department of Social Psychology and Quantitative Psychology, School of Psychology, Institute of Complex Systems, University of Barcelona, Barcelona, Spain
aff003; Perception and Awareness (PandA) Laboratory, Department of Neuroscience, Biomedicine and Movement Sciences, University of Verona, Verona, Italy
aff004; National Institute of Neuroscience, Verona, Italy
aff005
Vyšlo v časopise:
PLoS ONE 15(1)
Kategorie:
Research Article
doi:
https://doi.org/10.1371/journal.pone.0226816
Souhrn
The assessment of task-independent functional connectivity (FC) after a lesion causing hemianopia remains an uncovered topic and represents a crucial point to better understand the neural basis of blindsight (i.e. unconscious visually triggered behavior) and visual awareness. In this light, we evaluated functional connectivity (FC) in 10 hemianopic patients and 10 healthy controls in a resting state paradigm. The main aim of this study is twofold: first of all we focused on the description and assessment of density and intensity of functional connectivity and network topology with and without a lesion affecting the visual pathway, and then we extracted and statistically compared network metrics, focusing on functional segregation, integration and specialization. Moreover, a study of 3-cycle triangles with prominent connectivity was conducted to analyze functional segregation calculated as the area of each triangle created connecting three neighboring nodes. To achieve these purposes we applied a graph theory-based approach, starting from Pearson correlation coefficients extracted from pairs of regions of interest. In these analyses we focused on the FC extracted by the whole brain as well as by four resting state networks: The Visual (VN), Salience (SN), Attention (AN) and Default Mode Network (DMN), to assess brain functional reorganization following the injury. The results showed a general decrease in density and intensity of functional connections, that leads to a less compact structure characterized by decrease in functional integration, segregation and in the number of interconnected hubs in both the Visual Network and the whole brain, despite an increase in long-range inter-modules connections (occipito-frontal connections). Indeed, the VN was the most affected network, characterized by a decrease in intra- and inter-network connections and by a less compact topology, with less interconnected nodes. Surprisingly, we observed a higher functional integration in the DMN and in the AN regardless of the lesion extent, that may indicate a functional reorganization of the brain following the injury, trying to compensate for the general reduced connectivity. Finally we observed an increase in functional specialization (lower between-network connectivity) and in inter-networks functional segregation, which is reflected in a less compact network topology, highly organized in functional clusters. These descriptive findings provide new insight on the spontaneous brain activity in hemianopic patients by showing an alteration in the intrinsic architecture of a large-scale brain system that goes beyond the impairment of a single RSN.
Klíčová slova:
Brain damage – Clustering coefficients – Lesions – Network analysis – Neural networks – Occipital lobe – Vision – Visual impairments
Zdroje
1. Goodwin D. Homonymous hemianopia: challenges and solutions. Clin Ophthalmol. Dove Press; 2014;8: 1919–27. doi: 10.2147/OPTH.S59452 25284978
2. Zhang X, Kedar S, Lynn MJ, Newman NJ, Biousse V. Natural history of homonymous hemianopia. Neurology. 2006;66: 901–5. doi: 10.1212/01.wnl.0000203338.54323.22 16567709
3. Weiskrantz L, Warrington EK, Sanders MD, Marshall J. Visual capacity in the hemianopic field following a restricted occipital ablation. Brain. 1974;97: 709–28. doi: 10.1093/brain/97.1.709 4434190
4. Ajina S, Rees G, Kennard C, Bridge H. Abnormal Contrast Responses in the Extrastriate Cortex of Blindsight Patients. J Neurosci. 2015;35: 8201–8213. doi: 10.1523/JNEUROSCI.3075-14.2015 26019336
5. Bollini A, Sanchez-Lopez J, Savazzi S, Marzi CA. Lights from the dark: Neural responses from a blind visual hemifield. Front Neurosci. 2017;11: 1–14.
6. Bridge H, Hicks SL, Xie J, Okell TW, Mannan S, Alexander I, et al. Visual activation of extra-striate cortex in the absence of V1 activation. Neuropsychologia. 2010;48: 4148–4154. doi: 10.1016/j.neuropsychologia.2010.10.022 20974160
7. Ajina S, Pestilli F, Rokem A, Kennard C, Bridge H. Human blindsight is mediated by an intact geniculo-extrastriate pathway. Elife. 2015;4: 1–23. doi: 10.7554/eLife.08935 26485034
8. Murphy TH, Corbett D. Plasticity during stroke recovery: From synapse to behaviour. Nature Reviews Neuroscience. 2009; 10: 861–872. doi: 10.1038/nrn2735 19888284
9. Cramer SC, Sur M, Dobkin BH, O’Brien C, Sanger TD, Trojanowski JQ, et al. Harnessing neuroplasticity for clinical applications. Brain. 2011;134: 1591–1609. doi: 10.1093/brain/awr039 21482550
10. Grefkes C, Nowak DA, Eickhoff SB, Dafotakis M, Küst J, Karbe H, et al. Cortical connectivity after subcortical stroke assessed with functional magnetic resonance imaging. Ann Neurol. 2008;63: 236–246. doi: 10.1002/ana.21228 17896791
11. Sharma N, Baron JC, Rowe JB. Motor imagery after stroke: Relating outcome to motor network connectivity. Ann Neurol. 2009;66: 604–616. doi: 10.1002/ana.21810 19938103
12. Darian-Smith C, Gilbert C. Topographic reorganization in the striate cortex of the adult cat and monkey is cortically mediated. J Neurosci. 1995;15: 1631–1647. doi: 10.1523/JNEUROSCI.15-03-01631.1995 7891124
13. Das A, Gilbert CD. Receptive field expansion in adult visual cortex is linked to dynamic changes in strength of cortical connections. J Neurophysiol. 1995;74: 779–92. doi: 10.1152/jn.1995.74.2.779 7472382
14. Nelles G, Widman G, De Greiff A, Meistrowitz A, Dimitrova A, Weber J, et al. Brain representation of hemifield stimulation in poststroke visual field defects. Stroke. 2002;33: 1286–1293. doi: 10.1161/01.str.0000013685.76973.67 11988605
15. Baseler HA, Morland, AB, Wandell BA. Topographic organization of human visual areas in the absence of input from primary cortex. J Neuroscience. 1999;19(7): 2619–2627.
16. Mendola JD, Conner IP, Sharma S, Bahekar A, Lemieux S. fMRI measures of perceptual filling-in in the human visual cortex. J Cogn Neurosci. 2006;18: 363–375. doi: 10.1162/089892906775990624 16513002
17. Huxlin KR. Perceptual plasticity in damaged adult visual systems. Vision Res. 2008;48: 2154–2166. doi: 10.1016/j.visres.2008.05.022 18582488
18. Urbanski M, Coubard OA, Bourlon C. Visualizing the blind brain: brain imaging of visual field defects from early recovery to rehabilitation techniques. Front Integr Neurosci. 2014;8: 1–14.
19. Carter AR, Shulman GL, Corbetta M. Why use a connectivity-based approach to study stroke and recovery of function? Neuroimage. 2012;62: 2271–2280. doi: 10.1016/j.neuroimage.2012.02.070 22414990
20. Friston KJ. Functional and Effective Connectivity: A Review. Brain Connect. 2011;1: 13–36. doi: 10.1089/brain.2011.0008 22432952
21. Biswal BB, Van Kylen J, Hyde JS. Biswal B. B., van Kylen J. & Hyde J. S. Simultaneous assessment of flow and BOLD signals in Resting-State Functional Connectivity Maps. NMR Biomed. 1997;10: 165–170. doi: 10.1002/(sici)1099-1492(199706/08)10:4/5<165::aid-nbm454>3.0.co;2-7 9430343
22. Greicius MD, Krasnow B, Reiss AL, Menon V. Functional connectivity in the resting brain: A network analysis of the default mode hypothesis. Proc Natl Acad Sci. 2003;100: 253–258. doi: 10.1073/pnas.0135058100 12506194
23. Gusnard DA, Raichle ME. Searching for a baseline: Functional imaging and the resting human brain. Nat Rev Neurosci. 2001;2: 685–694. doi: 10.1038/35094500 11584306
24. Guo X, Jin Z, Feng X, Tong S, Member S. Enhanced Effective Connectivity in Mild Occipital Stroke Patients With Hemianopia. 2014;22: 1210–1217.
25. Wang L, Guo X, Sun J, Jin Z, Tong S. Cortical networks of hemianopia stroke patients: A graph theoretical analysis of EEG signals at resting state. Conf Proc IEEE Eng Med Biol Soc. 2012;2012:49–52. doi: 10.1109/EMBC.2012.6345868 23365829
26. Supekar K, Menon V, Rubin D, Musen M, Greicius MD. Network analysis of intrinsic functional brain connectivity in Alzheimer’s disease. PLoS Comput Biol. 2008;4. doi: 10.1371/journal.pcbi.1000100 18584043
27. Bassett DS, Nelson BG, Mueller BA, Camchong J, Lim KO. Altered Resting State Complexity in Schizophrenia. Neuroimage. 2012;59: 2196–2207. doi: 10.1016/j.neuroimage.2011.10.002 22008374
28. Liu Y, Liang M, Zhou Y, He Y, Hao Y, Song M, et al. Disrupted small-world networks in schizophrenia. Brain. 2008;131: 945–961. doi: 10.1093/brain/awn018 18299296
29. Zhang J, Wang J, Wu Q, Kuang W, Huang X, He Y, et al. Disrupted brain connectivity networks in drug-naive, first-episode major depressive disorder. Biol Psychiatry. 2011;70: 334–342. doi: 10.1016/j.biopsych.2011.05.018 21791259
30. Bohr IJ, Kenny E, Blamire A, O’Brien JT, Thomas AJ, Richardson J, et al. Resting-state functional connectivity in late-life depression: Higher global connectivity and more long distance connections. Front Psychiatry. Frontiers; 2013;3: 116. doi: 10.3389/fpsyt.2012.00116 23316175
31. Cocchi L, Harrison BJ, Pujol J, Harding IH, Fornito A, Pantelis C, et al. Functional alterations of large-scale brain networks related to cognitive control in obsessive-compulsive disorder. Hum Brain Mapp. 2012;33: 1089–1106. doi: 10.1002/hbm.21270 21612005
32. Müller RA, Shih P, Keehn B, Deyoe JR, Leyden KM, Shukla DK. Underconnected, but how? A survey of functional connectivity MRI studies in autism spectrum disorders. Cereb Cortex. 2011;21: 2233–2243. doi: 10.1093/cercor/bhq296 21378114
33. Fair DA, Posner J, Nagel BJ, Bathula D, Costa-Dias TG, Mills KL, et al. Atypical Default Network Connectivity in Youth with ADHD. Biol Psychiatry. 2010;68: 1084–1091.
34. Carter AR, Astafiev S V., Lang CE, Connor LT, Rengachary J, Strube MJ, et al. Resting interhemispheric functional magnetic resonance imaging connectivity predicts performance after stroke. Ann Neurol. 2010;67: 365–375. doi: 10.1002/ana.21905 20373348
35. Damoiseaux JS, Rombouts SARB, Barkhof F, Scheltens P, Stam CJ, Smith SM, et al. Consistent resting-state networks. 2006;
36. Fox MD, Raichle ME. Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging. Nat Rev Neurosci. 2007;8: 700–711. doi: 10.1038/nrn2201 17704812
37. van den Heuvel MP, Hulshoff Pol HE. Exploring the brain network: A review on resting-state fMRI functional connectivity. European Neuropsychopharmacology. 2010: 519–534. doi: 10.1016/j.euroneuro.2010.03.008 20471808
38. Raichle ME, MacLeod AM, Snyder AZ, Powers WJ, Gusnard DA, Shulman GL. A default mode of brain function. Proc Natl Acad Sci. 2001;98: 676–682. doi: 10.1073/pnas.98.2.676 11209064
39. Vemuri P, Jones DT, Jack CR. Resting state functional MRI in Alzheimer’s disease. Alzheimer’s Res Ther. 2012;4: 1–9. doi: 10.1186/alzrt100 22236691
40. Karbasforoushan H, Woodward ND. Resting-State Networks in Schizophrenia. Curr Top Med Chem. 2013;12: 2404–2414. doi: 10.2174/1568026611212210011
41. Smitha K, Akhil Raja K, Arun K, Rajesh P, Thomas B, Kapilamoorthy T, et al. Resting state fMRI: A review on methods in resting state connectivity analysis and resting state networks. Neuroradiol J. 2017;30: 305–317. doi: 10.1177/1971400917697342 28353416
42. Zhou Y, Friston KJ, Zeidman P, Chen J, Li S, Razi A. The Hierarchical Organization of the Default, Dorsal Attention and Salience Networks in Adolescents and Young Adults. 2018; 726–737. doi: 10.1093/cercor/bhx307 29161362
43. Huang CC, Hsieh WJ, Lee PL, Peng LN, Liu LK, Lee WJ, et al. Age-Related Changes in Resting-State Networks of A Large Sample Size of Healthy Elderly. CNS Neurosci Ther. 2015;21: 817–825. doi: 10.1111/cns.12396 25864728
44. Sporns O, Chialvo DR, Kaiser M, Hilgetag CC. Organization, development and function of complex brain networks. Trends in Cognitive Sciences. 2004: 418–425. doi: 10.1016/j.tics.2004.07.008 15350243
45. Reijneveld JC, Ponten SC, Berendse HW, Stam CJ. The application of graph theoretical analysis to complex networks in the brain. Clinical Neurophysiology. 2007: 2317–2331. doi: 10.1016/j.clinph.2007.08.010 17900977
46. Stam CJ, Reijneveld JC. Graph theoretical analysis of complex networks in the brain. Nonlinear Biomedical Physics. 2007; 3. doi: 10.1186/1753-4631-1-3 17908336
47. Kaiser M. A tutorial in connectome analysis: Topological and spatial features of brain networks. NeuroImage. 2011;57: 892–907. doi: 10.1016/j.neuroimage.2011.05.025 21605688
48. Higgins IA, Guo Y, Kundu S, Choi KS, Mayberg H. A Differential Degree Test for Comparing Brain Networks. 2018; 40: 1–35. doi: 10.1002/hbm.24718 31350786
49. Bassett DS, Xia CH, Satterthwaite TD. Understanding the Emergence of Neuropsychiatric Disorders With Network Neuroscience. Biol Psychiatry Cogn Neurosci Neuroimaging. Elsevier Inc; 2018;3: 742–753. doi: 10.1016/j.bpsc.2018.03.015 29729890
50. Braun U, Muldoon SF, Bassett DS. On Human Brain Networks in Health and Disease. 2015; doi: 10.1002/9780470015902.a0025783
51. Sporns O. Network attributes for segregation and integration in the human brain. Curr Opin Neurobiol. Elsevier Ltd; 2013;23: 162–171. doi: 10.1016/j.conb.2012.11.015 23294553
52. Tononi G, Sporns O, Edelman GM. A measure for brain complexity: Relating functional segregation and integration in the nervous system. Neurobiology. 1994; 91:5033–5037. doi: 10.1073/pnas.91.11.5033 8197179
53. Fox PT, Friston KJ. Distributed processing; distributed functions? Neuroimage. 2012;61(2):407–426. doi: 10.1016/j.neuroimage.2011.12.051 22245638
54. Watts D, Strogatz S. Collective dynamics of ‘small-world’ networks. Nature. 1998;393: 440–442. doi: 10.1038/30918 9623998
55. Latora V, Marchiori M. Efficient behavior of small-world networks. Phys Rev Lett. 2001;87: 198701–4. doi: 10.1103/PhysRevLett.87.198701 11690461
56. Bullmore E, Sporns O. Complex brain networks: Graph theoretical analysis of structural and functional systems. Nat Rev Neurosci. 2009;10: 186–198. doi: 10.1038/nrn2575 19190637
57. Folstein MF, Folstein SE, McHugh PR. “Mini-mental state”: A practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res. Pergamon; 1975;12: 189–198. doi: 10.1016/0022-3956(75)90026-6
58. Wilson B, Cockburn J, Halligan P. Development of a behavioral test of visuospatial neglect. Arch Phys Med Rehabil. 1987;68: 98–102. 3813864
59. Vallar G, Rusconi ML, Fontana S, Musicco M. Tre test di esplorazione visuo-spaziale: Taratura su 212 soggetti normali. Archivio Di Psicologia, Neurologia e Psichiatria. Arch Psicol Neurol Psichiatr. 1994;55: 827–841.
60. Gauthier L, Dehaut F, Joanette Y. The Bells Test: A quantitative and qualitative test for visual neglect. International Journal of Clinical Neuropsychology. 1989: 49–54.
61. Measso G, Cavarzeran F, Zappalà G, Lebowitz BD, Crook TH, Pirozzolo FJ, et al. The mini‐mental state examination: Normative study of an Italian random sample. Dev Neuropsychol. 1993;9: 77–85. doi: 10.1080/87565649109540545
62. Mangione CM, Lee PP, Gutierrez PR, Spritzer K, Berry S, Hays RD, et al. Development of the 25-item National Eye Institute Visual Function Questionnaire. Arch Ophthalmol (Chicago, Ill 1960). 2001;119: 1050–8.
63. Yushkevich PA, Piven J, Hazlett HC, Smith RG, Ho S, Gee JC, et al. User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage. 2006;31: 1116–1128. doi: 10.1016/j.neuroimage.2006.01.015 16545965
64. Rorden C, Brett M. Stereotaxic display of brain lesions. Behav Neurol. 2000;12: 191–200. doi: 10.1155/2000/421719 11568431
65. 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: 324–33. doi: 10.1097/00004728-199803000-00032 9530404
66. Collins DL, Holmes CJ, Peters TM, Evans AC. Automatic 3-D model-based neuroanatomical segmentation. Hum Brain Mapp. John Wiley & Sons, Ltd; 1995;3: 190–208. doi: 10.1002/hbm.460030304
67. Mazziotta J, Toga A, Evans A, Fox P, Lancaster J, Zilles K, et al. A probabilistic atlas and reference system for the human brain: International Consortium for Brain Mapping (ICBM). Philos Trans R Soc Lond B Biol Sci. 2001;356: 1293–322. doi: 10.1098/rstb.2001.0915 11545704
68. Wei J, Chen T, Li C, Liu G, Qiu J, Wei D. Eyes-open and eyes-closed resting states with opposite brain activity in sensorimotor and occipital regions: Multidimensional evidences from machine learning perspective. Front Hum Neurosci. 2018;12: 1–11.
69. Patriat R, Molloy EK, Meier TB, Kirk GR, Nair VA, Meyerand ME, et al. The effect of resting condition on resting-state fMRI reliability and consistency: A comparison between resting with eyes open, closed, and fixated. Neuroimage. 2013;78: 463–473. doi: 10.1016/j.neuroimage.2013.04.013 23597935
70. Diez I, Bonifazi P, Escudero I, Mateos B, Muñoz MA, Stramaglia S, et al. A novel brain partition highlights the modular skeleton shared by structure and function. Sci Rep. Nature.2015;5: 1–13. doi: 10.1038/srep10532 26037235
71. Ashburner J, Friston KJ. Nonlinear spatial normalization using Basis Functions. Hum Brain Mapp. 1999;7(4): 254–266. 10408769
72. Power JD, Barnes K, Snyder A. Spurious but systematic correlations in resting state functional connectivity MRI arise from head motion. Neuroimage. 2012;59: 2142–2154.
73. Bullmore ET, Bassett DS. Brain Graphs: Graphical Models of the Human Brain Connectome. Annu.Rev.Clin.Psychol. 2011; 7:113–140. doi: 10.1146/annurev-clinpsy-040510-143934 21128784
74. Tzourio-Mazoyer N, Landeau B, Papathanassiou D, Crivello F, Etard O, Delcroix N, et al. Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage. 2002;15: 273–289. doi: 10.1006/nimg.2001.0978 11771995
75. Fox MD, Zhang D, Snyder AZ, Raichle ME. The Global Signal and Observed Anticorrelated Resting State Brain Networks. J Neurophysiol. 2009;101: 3270–3283. doi: 10.1152/jn.90777.2008 19339462
76. Xia M, Wang J, He Y. BrainNet Viewer: A Network Visualization Tool for Human Brain Connectomics. PLoS One. 2013;8. doi: 10.1371/journal.pone.0068910 23861951
77. Fair DA, Dosenbach NUF, Church JA, Cohen AL, Brahmbhatt S, Miezin FM, et al. Development of distinct control networks through segregation and integration. Proc Natl Acad Sci. 2007;104: 13507–13512. doi: 10.1073/pnas.0705843104 17679691
78. van den Heuvel MP, de Lange SC, Zalesky A, Seguin C, Yeo BTT, Schmidt R. Proportional thresholding in resting-state fMRI functional connectivity networks and consequences for patient-control connectome studies: Issues and recommendations. Neuroimage. 2017;152: 437–449. doi: 10.1016/j.neuroimage.2017.02.005 28167349
79. Zalesky A, Fornito A, Cocchi L, Gollo LL, van den Heuvel MP, Breakspear M. Connectome sensitivity or specificity: which is more important? Neuroimage. 2016;142: 407–420. doi: 10.1016/j.neuroimage.2016.06.035 27364472
80. Rubinov M, Sporns O. Complex network measures of brain connectivity: Uses and interpretations. Neuroimage. 2010;52: 1059–1069. doi: 10.1016/j.neuroimage.2009.10.003 19819337
81. Fornito A, Zalesky A, Bullmore ET. Fundamentals of brain network analysis. 1st ed. Academic Press, 2016.
82. Bijsterbosch J, Smith S, Beckmann C. Introduction to resting state fMRI functional connectivity. 1st ed. Oxford University Press, 2017.
83. Farras-Permanyer L, Mancho-Fora N, Montalà-Flaquer M, Bartrés-Faz D, Vaqué-Alcázar L, Peró-Cebollero M, et al. Age-related changes in resting-state functional connectivity in older adults. Neural Regen Res. 2019;14: 1544–1555. doi: 10.4103/1673-5374.255976 31089053
84. Fruchterman TMJ, Reingold EM. Graph drawing by force‐directed placement. Softw Pract Exp. Wiley-Blackwell; 1991;21: 1129–1164. doi: 10.1002/spe.4380211102
85. Epskamp S, Cramer AOJ, Waldorp LJ, Schmittmann VD, Borsboom D. qgraph : Network Visualizations of Relationships in Psychometric Data. J Stat Softw. 2012;48. doi: 10.18637/jss.v048.i04
86. Dennis EL, Thompson PM. Functional brain connectivity using fMRI in aging and Alzheimer’s disease. Neuropsychology Review. 2014;24: 49–62. doi: 10.1007/s11065-014-9249-6 24562737
87. Kim Y-H, Cho A-H, Kim D, Kim SM, Lim HT, Kwon SU, et al. Early Functional Connectivity Predicts Recovery from Visual Field Defects after Stroke. J Stroke. 2019;21: 207–216. doi: 10.5853/jos.2018.02999 31161764
88. Wu GF, Brier MR, Parks C A-L, Ances BM, Van Stavern GP. An Eye on Brain Integrity: Acute Optic Neuritis Affects Resting State Functional Connectivity. Visual Neuroscience. 2015;56: 2541–2546. doi: 10.1167/iovs.14-16315 25813992
89. Chen L, Li S, Cai F, Wu L, Gong H, Pei C, et al. Altered functional connectivity density in primary angle-closure glaucoma patients at resting-state. Quant Imaging Med Surg. 2019;9: 603–614. doi: 10.21037/qims.2019.04.13 31143651
90. He BJ, Snyder AZ, Vincent JL, Epstein A, Shulman GL, Corbetta M. Breakdown of Functional Connectivity in Frontoparietal Networks Underlies Behavioral Deficits in Spatial Neglect. Neuron. 2007;53: 905–918. doi: 10.1016/j.neuron.2007.02.013 17359924
91. Menon V. Large-scale brain networks and psychopathology: A unifying triple network model. Trends Cogn Sci. Elsevier Ltd; 2011;15: 483–506. doi: 10.1016/j.tics.2011.08.003 21908230
92. Menon V. Salience Network. In: Toga Arthur W., editor. Brain Mapping: An Encyclopedic Reference; 2015, 2. pp. 597–611. Academic Press: Elsevier.
93. Damoiseaux JS, Beckmann CF, Arigita EJS, Barkhof F, Scheltens P, Stam CJ, et al. Reduced resting-state brain activity in the ‘default network’ in normal aging. Cereb Cortex. 2008;18: 1856–1864. doi: 10.1093/cercor/bhm207 18063564
94. Esposito F, Aragri A, Pesaresi I, Cirillo S, Tedeschi G, Marciano E, et al. Independent component model of the default-mode brain function: combining individual-level and population-level analyses in resting-state fMRI. Magn Reson Imaging. 2008;26: 905–913. doi: 10.1016/j.mri.2008.01.045 18486388
95. Andrews-Hanna JR, Snyder AZ, Vincent JL, Lustig C, Head D, Raichle ME, et al. Disruption of Large-Scale Brain Systems in Advanced Aging. Neuron. 2007;56: 924–935. doi: 10.1016/j.neuron.2007.10.038 18054866
96. Antonenko D, Flöel A. Healthy aging by staying selectively connected: A mini-review. Gerontology. 2013;60: 3–9. doi: 10.1159/000354376 24080587
97. Binnewijzend MAA, Schoonheim MM, Sanz-Arigita E, Wink AM, van der Flier WM, Tolboom N, et al. Resting-state fMRI changes in Alzheimer’s disease and mild cognitive impairment. Neurobiol Aging. Elsevier Inc.; 2012;33: 2018–2028. doi: 10.1016/j.neurobiolaging.2011.07.003 21862179
98. Sheline YI, Raichle ME. Resting State Functional Connectivity in Preclinical Alzheimer’s Disease: A Review. Biol Psychiatry. 2013;74: 340–347.
99. Greicius MD, Flores BH, Menon V, Glover GH, Solvason HB, Kenna H, et al. Resting-State Functional Connectivity in Major Depression: Abnormally Increased Contributions from Subgenual Cingulate Cortex and Thalamus. Biol Psychiatry. 2007;62: 429–437. doi: 10.1016/j.biopsych.2006.09.020 17210143
100. Ng KK, Lo JC, Lim JKW, Chee MWL, Zhou J. Reduced functional segregation between the default mode network and the executive control network in healthy older adults: A longitudinal study. Neuroimage.2016;133: 321–330. doi: 10.1016/j.neuroimage.2016.03.029 27001500
101. Menon V, Uddin LQ. Saliency, switching, attention and control: a network model of insula function. Brain Struct Funct. 2010;214: 655–667. doi: 10.1007/s00429-010-0262-0 20512370
102. Mattfeld AT, Gabrieli JDE, Biederman J, Spencer T, Brown A, Kotte A, et al. Brain differences between persistent and remitted attention deficit hyperactivity disorder. Brain. 2014;137: 2423–2428. doi: 10.1093/brain/awu137 24916335
103. Anderson JS, Nielsen JA, Ferguson MA, Burback MC, Cox ET, Dai L, et al. Abnormal brain synchrony in Down Syndrome. NeuroImage Clin. 2013;2: 703–715. doi: 10.1016/j.nicl.2013.05.006 24179822
104. Boucard CC, Rauschecker JP, Neufang S, Berthele A, Doll A, Manoliu A. Visual imagery and functional connectivity in blindness: a single-case study. Brain Struct Funct. 2016;221: 2367–2374. doi: 10.1007/s00429-015-1010-2 25690326
105. Bloom JS, Hynd GW. The role of the corpus callosum in interhemispheric transfer of information: Excitation or inhibition? Neuropsychol Rev. 2005;15: 59–71. doi: 10.1007/s11065-005-6252-y 16211466
106. Farràs-Permanyer L, Guàrdia-Olmos J, Peró-Cebollero M. Mild cognitive impairment and fMRI studies of brain functional connectivity: the state of the art. Front Psychol. 2015;6: 1–18.
107. Birn RM. The role of physiological noise in resting-state functional connectivity. NeuroImage. 2012. pp. 864–870. doi: 10.1016/j.neuroimage.2012.01.016 22245341
108. Birn RM, Diamond JB, Smith MA, Bandettini PA. Separating respiratory-variation-related fluctuations from neuronal-activity-related fluctuations in fMRI. Neuroimage. 2006;31: 1536–1548. doi: 10.1016/j.neuroimage.2006.02.048 16632379
109. Quigley M, Cordes D, Wendt G, Turski P, Moritz C, Haughton V, et al. Effect of focal and nonfocal cerebral lesions on functional connectivity studied with MR imaging. AJNR Am J Neuroradiol. 2001;22: 294–300. 11156772
110. Sbardella E, Tona F, Petsas N, Upadhyay N, Piattella MC, Filippini N, et al. Functional connectivity changes and their relationship with clinical disability and white matter integrity in patients with relapsing-remitting multiple sclerosis. Mult Scler J. 2015;21: 1681–1692. doi: 10.1177/1352458514568826 26041799
111. Tahedl M, Levine SM, Greenlee MW, Weissert R, Schwarzbach J V. Functional Connectivity in Multiple Sclerosis: Recent Findings and Future Directions. Front Neurol. 2018;9: 1–18.
Článek vyšel v časopise
PLOS One
2020 Číslo 1
- S diagnostikou Parkinsonovy nemoci může nově pomoci AI nástroj pro hodnocení mrkacího reflexu
- Proč při poslechu některé muziky prostě musíme tančit?
- Je libo čepici místo mozkového implantátu?
- Chůze do schodů pomáhá prodloužit život a vyhnout se srdečním chorobám
- Pomůže v budoucnu s triáží na pohotovostech umělá inteligence?
Nejčtenější v tomto čísle
- Severity of misophonia symptoms is associated with worse cognitive control when exposed to misophonia trigger sounds
- Chemical analysis of snus products from the United States and northern Europe
- Calcium dobesilate reduces VEGF signaling by interfering with heparan sulfate binding site and protects from vascular complications in diabetic mice
- Effect of Lactobacillus acidophilus D2/CSL (CECT 4529) supplementation in drinking water on chicken crop and caeca microbiome