Connectivity differences between Gulf War Illness (GWI) phenotypes during a test of attention

English version

Autoři: Tomas Clarke ^aff001; Jessie D. Jamieson ^aff002; Patrick Malone ^aff001; Rakib U. Rayhan ^aff003; Stuart Washington ^aff004; John W. VanMeter ^aff001; James N. Baraniuk ^aff004
Působiště autorů: Center for Functional and Molecular Imaging, Georgetown University, Washington, DC, United States of America ^aff001; Department of Mathematics, University of Nebraska-Lincoln, Lincoln, Nebraska, United States of America ^aff002; Department of Physiology and Biophysics, Howard University College of Medicine, Washington, DC, United States of America ^aff003; Division of Rheumatology, Immunology and Allergy, Georgetown University, Washington, DC, United States of America ^aff004
Vyšlo v časopise: PLoS ONE 14(12)
Kategorie: Research Article
prolekare.web.journal.doi_sk: https://doi.org/10.1371/journal.pone.0226481

Souhrn

One quarter of veterans returning from the 1990–1991 Persian Gulf War have developed Gulf War Illness (GWI) with chronic pain, fatigue, cognitive and gastrointestinal dysfunction. Exertion leads to characteristic, delayed onset exacerbations that are not relieved by sleep. We have modeled exertional exhaustion by comparing magnetic resonance images from before and after submaximal exercise. One third of the 27 GWI participants had brain stem atrophy and developed postural tachycardia after exercise (START: Stress Test Activated Reversible Tachycardia). The remainder activated basal ganglia and anterior insulae during a cognitive task (STOPP: Stress Test Originated Phantom Perception). Here, the role of attention in cognitive dysfunction was assessed by seed region correlations during a simple 0-back stimulus matching task (“see a letter, push a button”) performed before exercise. Analysis was analogous to resting state, but different from psychophysiological interactions (PPI). The patterns of correlations between nodes in task and default networks were significantly different for START (n = 9), STOPP (n = 18) and control (n = 8) subjects. Edges shared by the 3 groups may represent co-activation caused by the 0-back task. Controls had a task network of right dorsolateral and left ventrolateral prefrontal cortex, dorsal anterior cingulate cortex, posterior insulae and frontal eye fields (dorsal attention network). START had a large task module centered on the dorsal anterior cingulate cortex with direct links to basal ganglia, anterior insulae, and right dorsolateral prefrontal cortex nodes, and through dorsal attention network (intraparietal sulci and frontal eye fields) nodes to a default module. STOPP had 2 task submodules of basal ganglia–anterior insulae, and dorsolateral prefrontal executive control regions. Dorsal attention and posterior insulae nodes were embedded in the default module and were distant from the task networks. These three unique connectivity patterns during an attention task support the concept of Gulf War Disease with recognizable, objective patterns of cognitive dysfunction.

Klíčová slova:

Network analysis – Cognitive impairment – Prefrontal cortex – Eyes – Centrality – Attention – Cingulate cortex – Basal ganglia

Zdroje

1. Research Advisory Committee on Gulf War Veterans’ Illnesses. Gulf War Illness and the Health of Gulf War Veterans 2008. US Government Printing Office, Washington, DC pp. 29–30, p. 57. http://www.va.gov/rac-gwvi/docs/committee_documents/gwiandhealthofgwveterans_rac-gwvireport_2008.pdf Accessed Nov. 4, 2016

2. Fukuda K, Nisenbaum R, Stewart G, Thompson WW, Robin L, Washko RM, et al. Chronic multisymptom illness affecting Air Force veterans of the Gulf War. JAMA. 1998;280:981–8. doi: 10.1001/jama.280.11.981 9749480

3. Steele L. Prevalence and patterns of Gulf War illness in Kansas veterans: association of symptoms with characteristics of person, place, and time of military service. Am J Epidemiol. 2000;152:992–1002. doi: 10.1093/aje/152.10.992 11092441

4. Rayhan RU, Stevens BW, Raksit MP, Ripple JA, Timbol CR, Adewuyi O, VanMeter JW, Baraniuk JN. Exercise challenge in Gulf War Illness reveals two subgroups with altered brain structure and function. PLoS One 2013;8:e63903. doi: 10.1371/journal.pone.0063903 23798990

5. Owen AM, McMillan KM, Laird AR, Bullmore E. N-back working memory paradigm: a meta-analysis of normative functional neuroimaging studies. Hum Brain Mapp. 2005;25:46–59. doi: 10.1002/hbm.20131 15846822

6. Golomb BA. Acetylcholinesterase inhibitors and Gulf War illnesses. Proc Natl Acad Sci U S A. 2008;105:4295–300. doi: 10.1073/pnas.0711986105 18332428

7. Steele L, Sastre A, Gerkovich MM, Cook MR. Complex factors in the etiology of Gulf War illness: wartime exposures and risk factors in veteran subgroups. Environ Health Perspect. 2012;120:112–8. doi: 10.1289/ehp.1003399 21930452

8. White RF, Steele L, O’Callaghan JP, Sullivan K, Binns JH, Golomb BA, et al. Recent research on Gulf War illness and other health problems in veterans of the 1991 Gulf War: Effects of toxicant exposures during deployment. Cortex. 2016;74:449–75. doi: 10.1016/j.cortex.2015.08.022 26493934

9. Emmerich T, Zakirova Z, Klimas N, Sullivan K, Shetty AK, Evans JE, et al. Phospholipid profiling of plasma from GW veterans and rodent models to identify potential biomarkers of Gulf War Illness. PLoS One. 2017;12:e0176634. doi: 10.1371/journal.pone.0176634 28453542

10. Mesulam MM. The cholinergic innervation of the human cerebral cortex. Prog Brain Res. 2004;145:67–78. doi: 10.1016/S0079-6123(03)45004-8 14650907

11. Steele L, Lockridge O, Gerkovich MM, Cook MR, Sastre A. Butyrylcholinesterase genotype and enzyme activity in relation to Gulf War illness: preliminary evidence of gene-exposure interaction from a case-control study of 1991 Gulf War veterans. Environ Health 2015;14:4. doi: 10.1186/1476-069X-14-4 25575675

12. Haines DD, Ottenweller JE, Dickens BF, Mahmoud FF, Levine PH. Activity of Paraoxonase/Arylesterase and Butyrylcholinesterase in Peripheral Blood of Gulf War Era Veterans With Neurologic Symptom Complexes or Post-Traumatic Stress Disorder. J Occup Environ Med. 2017;59:1000–1006. doi: 10.1097/JOM.0000000000001129 28991135

13. Haley RW, Charuvastra E, Shell WE, Buhner DM, Marshall WW, Biggs MM, et al. Cholinergic autonomic dysfunction in veterans with Gulf War illness: confirmation in a population-based sample. JAMA Neurol. 2013;70:191–200. doi: 10.1001/jamaneurol.2013.596 23407784

14. Freeman R, Wieling W, Axelrod FB, Benditt DG, Benarroch E, Biaggioni I, et al. Consensus statement on the definition of orthostatic hypotension, neurally mediated syncope and the postural tachycardia syndrome. Clin Auton Res. 2011;21:69–72. doi: 10.1007/s10286-011-0119-5 21431947

15. Barnden LR, Kwiatek R, Crouch B, Burnet R, Del Fante P. Autonomic correlations with MRI are abnormal in the brainstem vasomotor centre in Chronic Fatigue Syndrome. Neuroimage Clin. 2016;11:530–7. doi: 10.1016/j.nicl.2016.03.017 27114901

16. Godoy L, Rossignoli M, Delfino-Pereira P, Garcia-Cairasco N, de Lima Umeoka EH. A Comprehensive Overview on Stress Neurobiology: Basic Concepts and Clinical Implications. Front. Behav. Neurosci., 3 July 2018. doi: 10.3389/fnbeh.2018.00127 30034327

17. Samuels ER, Szabadi E. Functional neuroanatomy of the noradrenergic locus coeruleus: its roles in the regulation of arousal and autonomic function part II: physiological and pharmacological manipulations and pathological alterations of locus coeruleus activity in humans. Curr Neuropharmacol. 2008;6:254–85. doi: 10.2174/157015908785777193 19506724

18. Bracha HS, Garcia-Rill E, Mrak RE, Skinner R. Postmortem locus coeruleus neuron count in three American veterans with probable or possible war-related PTSD. J Neuropsychiatry Clin Neurosci. 2005;17:503–9. doi: 10.1176/appi.neuropsych.17.4.503 16387990

19. Cowan N. The magical number 4 in short-term memory: a reconsideration of mental storage capacity. Behav Brain Sci. 2001;24:87–114. doi: 10.1017/s0140525x01003922 11515286

20. Miller GA. The magical number seven plus or minus two: some limits on our capacity for processing information. Psychol Rev. 1956;63:81–97. 13310704

21. Schneider-Garces NJ, Gordon BA, Brumback-Peltz CR, Shin E, Lee Y, Sutton BP, Maclin EL, et al. Span, CRUNCH, and beyond: working memory capacity and the aging brain. J Cogn Neurosci. 2010;22:655–69. doi: 10.1162/jocn.2009.21230 19320550

22. Sander MC, Lindenberger U, Werkle-Bergner M. Lifespan age differences in working memory: a two-component framework. Neurosci Biobehav Rev. 2012;36:2007–33. doi: 10.1016/j.neubiorev.2012.06.004 22771333

23. Nagel IE, Preuschhof C, Li SC, Nyberg L, Bäckman L, Lindenberger U, et al. Load modulation of BOLD response and connectivity predicts working memory performance in younger and older adults. J Cogn Neurosci. 2011;23:2030–45. doi: 10.1162/jocn.2010.21560 20828302

24. Oberauer K. The focus of attention in working memory-from metaphors to mechanisms. Front Hum Neurosci. 2013; 7:673. doi: 10.3389/fnhum.2013.00673 24146644

25. Rayhan RU, Washington SD, Garner R, Zajur K, Martinez Addiego F, VanMeter JW, et al. Exercise challenge alters Default Mode Network dynamics in Gulf War Illness. BMC Neuroscience. 2019 20:7. doi: 10.1186/s12868-019-0488-6 30791869

26. Zhang C, Zhou P, Yuan T. The cholinergic system in the cerebellum: from structure to function. Rev Neurosci. 2016;27:769–776. doi: 10.1515/revneuro-2016-0008 27559688

27. Romero-Romo JI, Bauer CC, Pasaye EH, Gutiérrez RA, Favila R, Barrios FA. Abnormal functioning of the thalamocortical system underlies the conscious awareness of the phantom limb phenomenon. Neuroradiol J. 2010;23:671–9. doi: 10.1177/197140091002300605 24148720

28. De Ridder D, Vanneste S, Freeman W. The Bayesian brain: phantom percepts resolve sensory uncertainty. Neurosci Biobehav Rev 2014;44:4–15. doi: 10.1016/j.neubiorev.2012.04.001 22516669

29. Stephenson AR, Edler MK, Erwin JM, Jacobs B, Hopkins WD, Hof PR et al. Cholinergic innervation of the basal ganglia in humans and other anthropoid primates. J Comp Neurol. 2017;525:319–332. doi: 10.1002/cne.24067 27328754

30. Eisinger RS, Urdaneta ME, Foote KD, Okun MS, Gunduz A. Non-motor Characterization of the Basal Ganglia: Evidence From Human and Non-human Primate Electrophysiology. Front. Neurosci., 5 July 2018. https://doi.org/10.3389/fnins.2018.00385

31. Shirer WR, Ryali S, Rykhlevskaia E, Menon V, Greicius MD. Decoding subject-driven cognitive states with whole-brain connectivity patterns. Cereb Cortex. 2012;22:158–65. doi: 10.1093/cercor/bhr099 21616982

32. Mesmoudi S, Perlbarg V, Rudrauf D, Messe A, Pinsard B, Hasboun D, et al. Resting state networks’ corticotopy: the dual intertwined rings architecture. PLoS One 2013;8:e67444. doi: 10.1371/journal.pone.0067444 23894288

33. Cioli C, Abdi H, Beaton D, Burnod Y, Mesmoudi S. Differences in human cortical gene expression match the temporal properties of large-scale functional networks. PLoS One 2014;9:e115913. doi: 10.1371/journal.pone.0115913 25546015

34. Power JD, Cohen AL, Nelson SM, Wig GS, Barnes KA, Church JA, et al. Functional network organization of the human brain. Neuron. 2011;72:665–78. doi: 10.1016/j.neuron.2011.09.006 22099467

35. Piccoli T, Valente G, Linden DE, Re M, Esposito F, Sack AT et al. The default mode network and the working memory network are not anti-correlated during all phases of a working memory task. PLoS One. 2015;10:e0123354. doi: 10.1371/journal.pone.0123354 25848951

36. Girvan M, Newman ME. Community structure in social and biological networks. Proc Natl Acad Sci U S A. 2002;99:7821–6. doi: 10.1073/pnas.122653799 12060727

37. Newman ME. Modularity and community structure in networks. Proc Natl Acad Sci U S A. 2006;103:8577–82. doi: 10.1073/pnas.0601602103 16723398

38. Bullmore E, Barnes A, Bassett DS, Fornito A, Kitzbichler M, Meunier D, et al. Generic aspects of complexity in brain imaging data and other biological systems. Neuroimage. 2009;47:1125–34. doi: 10.1016/j.neuroimage.2009.05.032 19460447

39. O’Reilly JX, Woolrich MW, Behrens TE, Smith SM, Johansen-Berg H. Tools of the trade: psychophysiological interactions and functional connectivity. Soc Cogn Affect Neurosci. 2012;7:604–9. doi: 10.1093/scan/nss055 22569188

40. Cole DM, Smith SM, Beckmann CF. Advances and pitfalls in the analysis and interpretation of resting-state FMRI data. Front Syst Neurosci 2010;4:8. doi: 10.3389/fnsys.2010.00008 20407579

41. Zuo N, Yang Z, Liu Y, Li J, Jiang T. Core networks and their reconfiguration patterns across cognitive loads. Hum Brain Mapp 2018; 39:3546–3557. doi: 10.1002/hbm.24193 29676536

42. Rayhan RU, Stevens BW, Timbol CR, Adewuyi O, Walitt B, VanMeter JW, et al. Increased brain white matter axial diffusivity associated with fatigue, pain and hyperalgesia in Gulf War illness. PLoS One 2013;8:e58493. doi: 10.1371/journal.pone.0058493 23526988

43. Baraniuk JN, El-Amin S, Corey R, Rayhan R, Timbol C. Carnosine treatment for gulf war illness: a randomized controlled trial. Glob J Health Sci 2013;5:69–81. doi: 10.5539/gjhs.v5n3p69 23618477

44. Smets EM, Garssen B, Bonke B, De Haes JC. The Multidimensional Fatigue Inventory (MFI) psychometric qualities of an instrument to assess fatigue. J Psychosom Res. 1995;39:315–25. doi: 10.1016/0022-3999(94)00125-o 7636775

45. Radloff LS. The CES-D Scale: A self-report depression scale for research in the general population. Applied Psychological Measurement 1977;1:385–401,

46. Geisser ME, Roth RS, Robinson ME. Assessing depression among persons with chronic pain using the Center for Epidemiological Studies-Depression Scale and the Beck Depression Inventory: a comparative analysis. Clin J Pain 1997;13:163–170. doi: 10.1097/00002508-199706000-00011 9186024

47. Löwe B, Decker O, Müller S, Brähler E, Schellberg D, Herzog W, et al. Validation and standardization of the Generalized Anxiety Disorder Screener (GAD-7) in the general population. Med Care. 2008;46:266–274. doi: 10.1097/MLR.0b013e318160d093 18388841

48. Craig KJ, Hietanen H, Markova IS, Berrios GE. The Irritability Questionnaire: a new scale for the measurement of irritability. Psychiatry Res. 2008;159:367–375. doi: 10.1016/j.psychres.2007.03.002 18374422

49. Sullivan MJL, Bishop SR, Pivik J. The pain catastrophizing scale: Development and validation. Psychological Assessment 1995;7:524–532

50. Baraniuk JN, Clauw DJ, Gaumond E. Rhinitis symptoms in chronic fatigue syndrome. Ann Allergy Asthma Immunol. 1998;81:359–65. doi: 10.1016/S1081-1206(10)63129-8 9809501

51. Melzack R. The short-form McGill pain questionnaire. Pain 1987;30:191–197. doi: 10.1016/0304-3959(87)91074-8 3670870

52. Ware JE, Sherbourne CD. The MOS 36-item short-form health survey (SF-36) I. Conceptual framework and item selection. Medical Care 1995;30:473–483.

53. Wolfe F, Smythe HA, Yunus MB, Bennett RM, Bombardier C, Goldenberg DL, et al. The American College of Rheumatology 1990 Criteria for the Classification of Fibromyalgia. Report of the Multicenter Criteria Committee. Arthritis Rheum. 1990;33:160–72. doi: 10.1002/art.1780330203 2306288

54. Naranch K, Park Y-J, Repka-Ramirez SM, Velarde A, Clauw D, Baraniuk JN. A tender sinus does not always mean sinusitis. Otolaryngol Head Neck Surg 2002;127:387–97 doi: 10.1067/mhn.2002.129038 12447232

55. http://www.pstnet.com/eprime.cfm

56. Walitt B, Čeko M, Khatiwada M, Gracely JL, Rayhan R, VanMeter JW, et al. Characterizing "fibrofog": Subjective appraisal, objective performance, and task-related brain activity during a working memory task. Neuroimage Clin 2016;11:173–80. doi: 10.1016/j.nicl.2016.01.021 26955513

57. RRID: SCR_007037

58. http://www.fil.ion.ucl.ac.uk/spm/software/spm5/

59. Penny W, Friston K, Ashburner J, Kiebel S, Nichols T. eds. Statistical Parametric Mapping: The Analysis of Functional Brain Images. 1st Edition. eBook ISBN: 9780080466507 Academic Press 2006

60. RRID: SCR_002403

61. https://www.nitrc.org/projects/mricron

62. MARSeille Boîte À Région d’Intérêt, RRID: SCR_009605; www.mrc-cbu.cam.ac.uk/Imaging/marsbar.html

63. 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;15:273–89. doi: 10.1006/nimg.2001.0978 11771995

64. RRID: SCR_001622

65. Washington SD, Gordon EM, Brar J, Warburton S, Sawyer AT, Wolfe A, et al. Dysmaturation of the default mode network in autism. Hum Brain Mapp. 2014;35:1284–96. doi: 10.1002/hbm.22252 23334984

66. Kriegeskorte N, Simmons WK, Bellgowan PS, Baker CI. Circular analysis in systems neuroscience: the dangers of double dipping. Nat Neurosci 2009;12:535–40. doi: 10.1038/nn.2303 19396166

67. Kriegeskorte N, Lindquist MA, Nichols TE, Poldrack RA, Vul E. Everything you never wanted to know about circular analysis, but were afraid to ask. J Cereb Blood Flow Metab. 2010;30:1551–7. doi: 10.1038/jcbfm.2010.86 20571517

68. Benjamini Y, Heller R, Yekutieli D. Selective inference in complex research. Philos Transact A Math Phys Eng Sci. 2009;367:4255–4271.

69. Genovese CR, Lazar NA, Nichols T. Thresholding of statistical maps in functional neuroimaging using the false discovery rate. Neuroimage. 2002;15:870–8. doi: 10.1006/nimg.2001.1037 11906227

70. Cohen J. Statistical Power Analysis for the Behavioral Sciences. 2nd Edition. Lawrence Erlbaum Assoc., Hillsdale, NJ. 1988. ISBN 0-8058-0283-5.

71. Fried EI. The 52 symptoms of major depression: Lack of content overlap among seven common depression scales. J Affect Disord. 2017;208:191–197. doi: 10.1016/j.jad.2016.10.019 27792962

72. Lok KH, Shi L, Zhu X, Wang D. Fast and robust brain tumor segmentation using level set method with multiple image information. J Xray Sci Technol. 2017;25:301–312. doi: 10.3233/XST-17261 28269819

73. Shannon CE. Prediction and entropy of printed English. Bell system technical journal 1951;30:50–64.

74. Yao Y, Lu WL, Xu B, Li CB, Lin CP, Waxman D, Feng JF. The increase of the functional entropy of the human brain with age. Sci Rep. 2013;3:2853. doi: 10.1038/srep02853 24103922

75. Hagberg AA, Schult AA, Swart PJ. Exploring network structure, dynamics, and function using networkx. Varoquaux G, Vaught T, Millman J. Eds. Proceedings of the 7th Python in Science Conference (SciPy2008). Pasadena, CA USA. 2008. 11–15.

76. Stephenson K, Zelen M. Rethinking centrality: methods and examples. Social Networks 1989;11:1–37.

77. Rubinov M, Sporns O. Complex network measures of brain connectivity: uses and interpretations. Neuroimage 2010;52:1059–69. doi: 10.1016/j.neuroimage.2009.10.003 19819337

78. Joyce KE, Laurienti PJ, Burdette JH, Hayasaka S. A new measure of centrality for brain networks. PLoS One. 2010;5:e12200. doi: 10.1371/journal.pone.0012200 20808943

79. Blondel VD, Guillaume J-L, Lambiotte R, Lefebvre E. Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment. 2008;10: P10008.

80. http://vassarstats.net

81. Tucker LR. A method for synthesis of factor analysis studies. Personnel Research Section Report No. 984. Washington, DC: Department of the Army. 1951.

82. Lorenzo-Seva U, ten Berge JMF. Tucker’s Congruence Coefficient as a Meaningful Index of Factor Similarity. Methodology European Journal of Research Methods for the Behavioral and Social Sciences 2006;2:57–64.

83. 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;62:429–37. doi: 10.1016/j.biopsych.2006.09.020 17210143

84. Rzucidlo JK, Roseman PL, Laurienti PJ, Dagenbach D. Stability of whole brain and regional network topology within and between resting and cognitive states. PLoS One 2013;8:e70275. doi: 10.1371/journal.pone.0070275 23940554

85. Seghier ML. The angular gyrus: multiple function ad multiple subdivisions. Neuroscientist 2012;19:43–61. doi: 10.1177/1073858412440596 22547530

86. Arsalidou M, Taylor MJ. Is 2+2 = 4? Meta-analyses of brain areas needed for numbers and calculations. Neuroimage 2011;54:2382–2893. doi: 10.1016/j.neuroimage.2010.10.009 20946958

87. Chen Q, Weidner R, Vossel S, Weiss PH, Fink GR. Neural mechanisms of attentional reorienting in three-dimensional space. J Neurosci 2012;32:13352–62. doi: 10.1523/JNEUROSCI.1772-12.2012 23015426

88. Rottschy C, Langner R, Dogan I, Reetz K, Laird AR, Schulz JB, et al. Modelling neural correlates of working memory: a coordinate-based meta-analysis. Neuroimage 2012;60:830–46. doi: 10.1016/j.neuroimage.2011.11.050 22178808

89. Shulman GL, Astafiev SV, Franke D, Pope DL, Snyder AZ, McAvoy MP, et al. Interaction of stimulus-driven reorienting and expectation in ventral and dorsal frontoparietal and basal ganglia-cortical networks. J Neurosci. 2009;29:4392–407. doi: 10.1523/JNEUROSCI.5609-08.2009 19357267

90. Cieslik EC, Zilles K, Caspers S, Roski C, Kellermann TS, Jakobs O, et al. Is there "one" DLPFC in cognitive action control? Evidence for heterogeneity from co-activation-based parcellation. Cereb Cortex 2013;23:2677–89. doi: 10.1093/cercor/bhs256 22918987

91. Anderson JS, Ferguson MA, Lopez-Larson M, Yurgelun-Todd D. Topographic maps of multisensory attention. Proc Natl Acad Sci U S A. 2010;107:20110–4. 21041658

92. Sebastian A, Jung P, Neuhoff J, Wibral M, Fox PT, Lieb K, et al. Dissociable attentional and inhibitory networks of dorsal and ventral areas of the right inferior frontal cortex: a combined task-specific and coordinate-based meta-analytic fMRI study. Brain Struct Funct 2016;221:1635–51. doi: 10.1007/s00429-015-0994-y 25637472

93. Chan AW, Downing PE. Faces and eyes in human lateral prefrontal cortex. Front Hum Neurosci. 2011;5:51. doi: 10.3389/fnhum.2011.00051 21687796

94. Laird AR, Fox PM, Eickhoff SB, Turner JA, Ray KL, McKay DR, et al. Behavioral interpretations of intrinsic connectivity networks. J Cogn Neurosci 2011;23:4022–37. doi: 10.1162/jocn_a_00077 21671731

95. Wager TD, Smith EE. Neuroimaging studies of working memory: a meta-analysis. Cogn Affect Behav Neurosci 2003;3:255–74. doi: 10.3758/cabn.3.4.255 15040547

96. D’Esposito M, Postle BR, Rypma B. Prefrontal cortical contributions to working memory: evidence from event-related fMRI studies. Exp Brain Res 2000;133:3–11. doi: 10.1007/s002210000395 10933205

97. Kurth F, Zilles K, Fox PT, Laird AR, Eickhoff SB. A link between the systems: functional differentiation and integration within the human insula revealed by meta-analysis. Brain Struct Funct 2010;214:519–34. doi: 10.1007/s00429-010-0255-z 20512376

98. Bastin J, Deman P, David O, Gueguen M, Benis D, Minotti L, et al. Direct Recordings from Human Anterior Insula Reveal its Leading Role within the Error-Monitoring Network. Cereb Cortex. 2016.

99. Eckert MA, Menon V, Walczak A, Ahlstrom J, Denslow W, Horwitz A, et al. At the heart of the ventral attention system: the right anterior insula. Hum Brain Mapp. 2009;30:2530–41. doi: 10.1002/hbm.20688 19072895

100. Patel GH, Yang D, Jamerson EC, Snyder LH, Corbetta M, Ferrera VP. Functional evolution of new and expanded attention networks in humans. Proc Natl Acad Sci U S A. 2015;112:9454–9. doi: 10.1073/pnas.1420395112 26170314

101. Burles F, Slone E, Iaria G. Dorso-medial and ventro-lateral functional specialization of the human retrosplenial complex in spatial updating and orienting. Brain Struct Funct. 2017;222:1481–1493. doi: 10.1007/s00429-016-1288-8 27553438

102. Leech R, Sharp DJ. The role of the posterior cingulate cortex in cognition and disease. Brain 2014;137:12–32. doi: 10.1093/brain/awt162 23869106

103. Andrews-Hanna JR, Smallwood J, Spreng RN. The default network and self-generated thought: component processes, dynamic control, and clinical relevance. Ann N Y Acad Sci. 2014;1316:29–52. doi: 10.1111/nyas.12360 24502540

104. Mittner M, Hawkins GE, Boekel W, Forstmann BU. A Neural Model of Mind Wandering. Trends Cogn Sci. 2016;20:570–8. doi: 10.1016/j.tics.2016.06.004 27353574

105. Boissoneault J, Letzen J, Lai S, O’Shea A, Craggs J, Robinson ME, et al. Abnormal resting state functional connectivity in patients with chronic fatigue syndrome: an arterial spin-labeling fMRI study. Magn Reson Imaging. 2016;34:603–8. doi: 10.1016/j.mri.2015.12.008 26708036

106. Cowan N, Rouder JN, Blume CL, Saults JS. Models of verbal working memory capacity: what does it take to make them work? Psychol Rev. 2012;119:480–99. doi: 10.1037/a0027791 22486726

107. Rich EL, Wallis JD. Decoding subjective decisions from orbitofrontal cortex. Nat Neurosci. 2016;19:973–80. doi: 10.1038/nn.4320 27273768

108. Bischoff-Grethe A, Buxton RB, Paulus MP, Fleisher AS, Yang TT, Brown GG. Striatal and Pallidal Activation during Reward Modulated Movement Using a Translational Paradigm. J Int Neuropsychol Soc 2015;21:399–411. doi: 10.1017/S1355617715000491 26156687

109. Peterson EJ, Seger CA. Many hats: intratrial and reward level-dependent BOLD activity in the striatum and premotor cortex. J Neurophysiol 2013;110:1689–702. doi: 10.1152/jn.00164.2012 23741040

110. Palomero-Gallagher N, Eickhoff SB, Hoffstaedter F, Schleicher A, Mohlberg H, Vogt BA, et al. Functional organization of human subgenual cortical areas: Relationship between architectonical segregation and connectional heterogeneity. Neuroimage 2015;115:177–90. doi: 10.1016/j.neuroimage.2015.04.053 25937490

111. Murrough JW, Abdallah CG, Anticevic A, Collins KA, Geha P, Averill LA, et al. Reduced global functional connectivity of the medial prefrontal cortex in major depressive disorder. Hum Brain Mapp. 2016;37:3214–23. doi: 10.1002/hbm.23235 27144347

112. Fitzgerald PB, Laird AR, Maller J, Daskalakis ZJ. A meta-analytic study of changes in brain activation in depression. Hum Brain Mapp 2008;29:683–95. doi: 10.1002/hbm.20426 17598168

113. Penner J, Ford KA, Taylor R, Schaefer B, Théberge J, Neufeld RWJ, et al. Medial Prefrontal and Anterior Insular Connectivity in Early Schizophrenia and Major Depressive Disorder: A Resting Functional MRI Evaluation of Large-Scale Brain Network Models. Front Hum Neurosci 2016;10:132. doi: 10.3389/fnhum.2016.00132 27064387

114. Alvarez RP, Kirlic N, Misaki M, Bodurka J, Rhudy JL, Paulus MP, et al. Increased anterior insula activity in anxious individuals is linked to diminished perceived control. Transl Psychiatry 2015;5:e591. doi: 10.1038/tp.2015.84 26125154

115. Waters AM, Bradley BP, Mogg K. Biased attention to threat in paediatric anxiety disorders (generalized anxiety disorder, social phobia, specific phobia, separation anxiety disorder) as a function of ‘distress’ versus ‘fear’ diagnostic categorization. Psychol Med 2014;44:607–16. doi: 10.1017/S0033291713000779 23591000

116. Bracha HS. Freeze, flight, fight, fright, faint: adaptationist perspectives on the acute stress response spectrum. CNS Spectr 2004;9:679–85. doi: 10.1017/s1092852900001954 15337864

117. Bracha HS. Human brain evolution and the "Neuroevolutionary Time-depth Principle:" Implications for the Reclassification of fear-circuitry-related traits in DSM-V and for studying resilience to warzone-related posttraumatic stress disorder. Prog Neuropsychopharmacol Biol Psychiatry 2006;30:827–53. doi: 10.1016/j.pnpbp.2006.01.008 16563589

118. Kozlowska K, Walker P, McLean L, Carrive P. Fear and the Defense Cascade: Clinical Implications and Management. Harv Rev Psychiatry 2015;23:263–87. doi: 10.1097/HRP.0000000000000065 26062169

119. Baur V, Hänggi J, Langer N, Jäncke L. Resting-state functional and structural connectivity within an insula-amygdala route specifically index state and trait anxiety. Biol Psychiatry 2013;73:85–92. doi: 10.1016/j.biopsych.2012.06.003 22770651

120. Mutschler I, Wieckhorst B, Kowalevski S, Derix J, Wentlandt J, Schulze-Bonhage A et al. Functional organization of the human anterior insular cortex. Neurosci Lett 2009;457:66–70. doi: 10.1016/j.neulet.2009.03.101 19429164

121. Craig AD. Forebrain emotional asymmetry: a neuroanatomical basis? Trends Cogn Sci. 2005;9:566–71. doi: 10.1016/j.tics.2005.10.005 16275155

122. Chao LL, Zhang Y, Buckley S. Effects of low-level sarin and cyclosarin exposure on white matter integrity in Gulf War Veterans. Neurotoxicology 2015;48:239–48. doi: 10.1016/j.neuro.2015.04.005 25929683

123. Harrison BJ, Fullana MA, Soriano-Mas C, Via E, Pujol J, Martinez-Zalacain I, et al. A neural mediator of human anxiety sensitivity. Hum Brain Mapp 2015;36:3950–8. doi: 10.1002/hbm.22889 26147233

124. Dehaene S, Changeux JP, Naccache L, Sackur J, Sergent C. Conscious, preconscious, and subliminal processing: a testable taxonomy. Trends Cogn Sci 2006;10:204–11. doi: 10.1016/j.tics.2006.03.007 16603406

125. Bastuji H, Frot M, Perchet C, Magnin M, Garcia-Larrea L. Pain networks from the inside: Spatiotemporal analysis of brain responses leading from nociception to conscious perception. Hum Brain Mapp 2016;37:4301–4315. doi: 10.1002/hbm.23310 27391083

126. Dehghan M, Schmidt-Wilcke T, Pfleiderer B, Eickhoff SB, Petzke F, Harris RE, et al. Coordinate-based (ALE) meta-analysis of brain activation in patients with fibromyalgia. Hum Brain Mapp 2016;37:1749–58. doi: 10.1002/hbm.23132 26864780

127. Butti C, Santos M, Uppal N, Hof PR. Von Economo neurons: clinical and evolutionary perspectives. Cortex 2013;49:312–26. doi: 10.1016/j.cortex.2011.10.004 22130090

128. Allman JM, Tetreault NA, Hakeem AY, Manaye KF, Semendeferi K, Erwin JM, et al. The von Economo neurons in the frontoinsular and anterior cingulate cortex. Ann N Y Acad Sci 2011;1225:59–71. doi: 10.1111/j.1749-6632.2011.06011.x 21534993

129. Prager EM, Bergstrom HC, Wynn GH, Braga MF. The basolateral amygdala γ-aminobutyric acidergic system in health and disease. J Neurosci Res 2016;94:548–67. doi: 10.1002/jnr.23690 26586374

130. Aroniadou-Anderjaska V, Figueiredo TH, Apland JP, Prager EM, Pidoplichko VI, Miller SL, et al. Long-term neuropathological and behavioral impairments after exposure to nerve agents. Ann N Y Acad Sci. 2016;1374:17–28. doi: 10.1111/nyas.13028 27002925

131. Prager EM, Pidoplichko VI, Aroniadou-Anderjaska V, Apland JP, Braga MF. Pathophysiological mechanisms underlying increased anxiety after soman exposure: reduced GABAergic inhibition in the basolateral amygdala. Neurotoxicology 2014;44:335–43. doi: 10.1016/j.neuro.2014.08.007 25150775

132. Almeida-Suhett CP, Prager EM, Pidoplichko V, Figueiredo TH, Marini AM, Li Z, et al. Reduced GABAergic inhibition in the basolateral amygdala and the development of anxiety-like behaviors after mild traumatic brain injury. PLoS One 2014;9:e102627. doi: 10.1371/journal.pone.0102627 25047645

133. Glasser MF, Coalson TS, Robinson EC, Hacker CD, Harwell J, Yacoub E, et al. A multi-modal parcellation of human cerebral cortex. Nature 2016;536:171–8. doi: 10.1038/nature18933 27437579

134. Čeko M, Gracely JL, Fitzcharles MA, Seminowicz DA, Schweinhardt P, Bushnell MC. Is a Responsive Default Mode Network Required for Successful Working Memory Task Performance? J Neurosci. 2015;35:11595–605. 26290236

135. van den Heuvel M, Mandl R, Hulshoff Pol H. Normalized cut group clustering of resting-state FMRI data. PLoS One. 2008;3:e2001. doi: 10.1371/journal.pone.0002001 18431486

136. Telesford QK, Simpson SL, Burdette JH, Hayasaka S, Laurienti PJ. The brain as a complex system: using network science as a tool for understanding the brain. Brain Connect. 2011;1:295–308. doi: 10.1089/brain.2011.0055 22432419

137. Zalesky A, Fornito A, Bullmore ET. Network-based statistic: identifying differences in brain networks. Neuroimage. 2010;53:1197–207. doi: 10.1016/j.neuroimage.2010.06.041 20600983

138. Nichols T, Hayasaka S. Controlling the familywise error rate in functional neuroimaging: a comparative review. Stat Methods Med Res. 2003;12:419–46. doi: 10.1191/0962280203sm341ra 14599004

139. Rosenberg MD, Hsu WT, Scheinost D, Constable R, Chun MM. Connectome-based Models Predict Separable Components of Attention in Novel Individuals. J Cogn Neurosci. 2018;30:160–173. doi: 10.1162/jocn_a_01197 29040013

140. http://bioimagesuite.yale.edu/mni2tal/

141. Xia M, Wang J, He Y. BrainNet Viewer: A Network Visualization Tool for Human Brain Connectomics. PLoS ONE 2013;8:e68910. doi: 10.1371/journal.pone.0068910 23861951

Connectivity differences between Gulf War Illness (GWI) phenotypes during a test of attention

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