Brain Topography

, Volume 30, Issue 4, pp 502–520 | Cite as

EEG Microstate Correlates of Fluid Intelligence and Response to Cognitive Training

  • Emiliano Santarnecchi
  • Arjun R. Khanna
  • Christian S. Musaeus
  • Christopher S. Y. Benwell
  • Paula Davila
  • Faranak Farzan
  • Santosh Matham
  • Alvaro Pascual-Leone
  • Mouhsin M. Shafi
  • on behalf of Honeywell SHARP Team authors
Original Paper
  • 468 Downloads

Abstract

The neurobiological correlates of human fluid intelligence (Gf) remain elusive. Here, we demonstrate that spatiotemporal dynamics of EEG activity correlate with baseline measures of Gf and with its modulation by cognitive training. EEG dynamics were assessed in 74 healthy participants by examination of fast-changing, recurring, topographically-defined electric patterns termed “microstates”, which characterize the electrophysiological activity of distributed cortical networks. We find that the frequency of appearance of specific brain topographies, spatially associated with visual (microstate B) and executive control (microstate C) networks, respectively, is inversely related to Gf scores. Moreover, changes in Gf scores with cognitive training are inversely correlated with changes in microstate properties, indicating that the changes in brain network dynamics are behaviorally relevant. Finally, we find that cognitive training that increases Gf scores results in a posterior shift in the topography of microstate C. These results highlight the role of fast-changing brain electrical states in individual variability in Gf and in the response to cognitive training.

Keywords

Fluid intelligence Abstract reasoning Microstates EEG Cognitive training 

Supplementary material

10548_2017_565_MOESM1_ESM.doc (47 kb)
Supplementary material 1 (DOC 47 KB)

References

  1. Alexander GE, Furey ML, Grady CL, Pietrini P, Brady DR, Mentis MJ, Schapiro MB (1997) Association of premorbid intellectual function with cerebral metabolism in Alzheimer’s disease: implications for the cognitive reserve hypothesis. Am J Psychiatry 154:165–172PubMedCrossRefGoogle Scholar
  2. Aron AR, Robbins TW, Poldrack RA (2014) Inhibition and the right inferior frontal cortex: one decade on. Trends Cogn Sci 18:177–185PubMedCrossRefGoogle Scholar
  3. Au J, Sheehan E, Tsai N, Duncan GJ, Buschkuehl M, Jaeggi SM (2014) Improving fluid intelligence with training on working memory: a meta-analysis. Psychon Bull Rev 22:366–377CrossRefGoogle Scholar
  4. Bartlett MS (1937) The statistical conception of mental factors. Br J Psychol 28:97–104Google Scholar
  5. Beaujean AA, Firmin MW, Michonski JD, Berry T, Johnson C (2010) A multitrait–multimethod examination of the reynolds intellectual assessment scales in a college sample. Assessment 17:347–360PubMedCrossRefGoogle Scholar
  6. Benedek M, Jauk E, Sommer M, Arendasy M, Neubauer AC (2014) Intelligence, creativity, and cognitive control: the common and differential involvement of executive functions in intelligence and creativity. Intelligence 46:73–83PubMedPubMedCentralCrossRefGoogle Scholar
  7. Bondarenko R, Boehler CN, Stoppel CM, Heinze HJ, Schoenfeld MA, Hopf JM (2012) Separable mechanisms underlying global feature-based attention. J Neurosci 32:15284–15295PubMedCrossRefGoogle Scholar
  8. Britz J, Van De Ville D, Michel CM (2010a) BOLD correlates of EEG topography reveal rapid resting-state network dynamics. Neuroimage 52:1162–1170PubMedCrossRefGoogle Scholar
  9. Britz J, Van De Ville D, Michel CM (2010b) BOLD correlates of EEG topography reveal rapid resting-state network dynamics. Neuroimage 52:1162–1170PubMedCrossRefGoogle Scholar
  10. Brumback CR, Low KA, Gratton G, Fabiani M (2004) Sensory ERPs predict differences in working memory span and fluid intelligence. Neuroreport 15:373–376PubMedCrossRefGoogle Scholar
  11. Brunet D, Murray MM, Michel CM (2011) Spatiotemporal analysis of multichannel EEG: CARTOOL. Comput Intell Neurosci 2011:813870PubMedPubMedCentralCrossRefGoogle Scholar
  12. Burgess GC, Gray JR, Conway AR, Braver TS (2011) Neural mechanisms of interference control underlie the relationship between fluid intelligence and working memory span. J Exp Psychol Gen 140:674–692PubMedPubMedCentralCrossRefGoogle Scholar
  13. Buschman TJ, Siegel M, Roy JE, Miller EK (2011) Neural substrates of cognitive capacity limitations. Proc Natl Acad Sci USA 108:11252–11255PubMedPubMedCentralCrossRefGoogle Scholar
  14. Carpenter PA, Just MA, Shell P (1990) What one intelligence test measures: a theoretical account of the processing in the Raven Progressive Matrices Test. Psychol Rev 97:404–431PubMedCrossRefGoogle Scholar
  15. Chiang MC, Barysheva M, Lee AD, Madsen S, Klunder AD, Toga AW, McMahon KL, de Zubicaray GI, Meredith M, Wright MJ, Srivastava A, Balov N, Thompson PM (2008) Brain fiber architecture, genetics, and intelligence: a high angular resolution diffusion imaging (HARDI) study. Med Image Comput Comput Assist Interv 11:1060–1067PubMedPubMedCentralGoogle Scholar
  16. Choi YY, Shamosh NA, Cho SH, Deyoung CG, Lee MJ, Lee JM, Kim SI, Cho ZH, Kim K, Gray JR, Lee KH (2008) Multiple bases of human intelligence revealed by cortical thickness and neural activation. J Neurosci 28:10323–10329PubMedCrossRefGoogle Scholar
  17. Cole JC, Lopez BR, Daleo DV (2004) Latent relationships of fluid, visual, and simultaneous cognitive tasks. Psychol Rep 94:547–561PubMedCrossRefGoogle Scholar
  18. Cole MW, Yarkoni T, Repovs G, Anticevic A, Braver TS (2012) Global connectivity of prefrontal cortex predicts cognitive control and intelligence. J Neurosci 32:8988–8999PubMedPubMedCentralCrossRefGoogle Scholar
  19. Colom R, Jung RE, Haier RJ (2006) Distributed brain sites for the g-factor of intelligence. Neuroimage 31:1359–1365PubMedCrossRefGoogle Scholar
  20. Colom R, Karama S, Jung RE, Haier RJ (2010) Human intelligence and brain networks. Dialogues Clin Neurosci 12:489–501PubMedPubMedCentralGoogle Scholar
  21. Colom R, Burgaleta M, Roman FJ, Karama S, Alvarez-Linera J, Abad FJ, Martinez K, Quiroga MA, Haier RJ (2013) Neuroanatomic overlap between intelligence and cognitive factors: morphometry methods provide support for the key role of the frontal lobes. Neuroimage 72:143–152PubMedCrossRefGoogle Scholar
  22. Crone EA, Wendelken C, van LL, Honomichl RD, Christoff K, Bunge SA (2009) Neurocognitive development of relational reasoning. Dev Sci 12:55–66PubMedPubMedCentralCrossRefGoogle Scholar
  23. da Rocha AF, Rocha FT, Massad E (2011) The brain as a distributed intelligent processing system: an EEG study. PLoS ONE 6:e17355PubMedPubMedCentralCrossRefGoogle Scholar
  24. Damoiseaux JS, Rombouts SA, Barkhof F, Scheltens P, Stam CJ, Smith SM, Beckmann CF (2006) Consistent resting-state networks across healthy subjects. Proc Natl Acad Sci USA 103:13848–13853PubMedPubMedCentralCrossRefGoogle Scholar
  25. Dang CP, Braeken J, Colom R, Ferrer E, Liu C (2014) Why is working memory related to intelligence? Different contributions from storage and processing. Memory 22:426–441PubMedCrossRefGoogle Scholar
  26. Deary I (2008) Why do intelligent people live longer? Nature 456:175–176PubMedCrossRefGoogle Scholar
  27. Deary IJ, Penke L, Johnson W (2010) The neuroscience of human intelligence differences. Nat Rev Neurosci 11:201–211PubMedGoogle Scholar
  28. Delorme A, Makeig S (2004) EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J Neurosci Methods 134:9–21PubMedCrossRefGoogle Scholar
  29. Engle RW, Tuholski SW, Laughlin JE, Conway AR (1999) Working memory, short-term memory, and general fluid intelligence: a latent-variable approach. J Exp Psychol Gen 128:309–331PubMedCrossRefGoogle Scholar
  30. Filmer HL, Dux PE, Mattingley JB (2014) Applications of transcranial direct current stimulation for understanding brain function. Trends Neurosci 37:742–753PubMedCrossRefGoogle Scholar
  31. Gaspar JM, McDonald JJ (2014) Suppression of salient objects prevents distraction in visual search. J Neurosci 34:5658–5666PubMedCrossRefGoogle Scholar
  32. Gazzaley A, Cooney JW, Rissman J, D’Esposito M (2005) Top-down suppression deficit underlies working memory impairment in normal aging. Nat Neurosci 8:1298–1300PubMedCrossRefGoogle Scholar
  33. Gazzaley A, Rissman J, Cooney J, Rutman A, Seibert T, Clapp W, D’Esposito M (2007) Functional interactions between prefrontal and visual association cortex contribute to top-down modulation of visual processing. Cereb Cortex 17(Suppl 1):i125–i135PubMedPubMedCentralCrossRefGoogle Scholar
  34. Goh S, Bansal R, Xu D, Hao X, Liu J, Peterson BS (2011) Neuroanatomical correlates of intellectual ability across the life span. Dev Cogn Neurosci 1:305–312PubMedCrossRefGoogle Scholar
  35. Gong QY, Sluming V, Mayes A, Keller S, Barrick T, Cezayirli E, Roberts N (2005) Voxel-based morphometry and stereology provide convergent evidence of the importance of medial prefrontal cortex for fluid intelligence in healthy adults. Neuroimage 25:1175–1186PubMedCrossRefGoogle Scholar
  36. Gray JR, Thompson PM (2004a) Neurobiology of intelligence: health implications? Discov Med 4:157–162PubMedGoogle Scholar
  37. Gray JR, Thompson PM (2004b) Neurobiology of intelligence: science and ethics. Nat Rev Neurosci 5:471–482PubMedCrossRefGoogle Scholar
  38. Gray JR, Chabris CF, Braver TS (2003) Neural mechanisms of general fluid intelligence. Nat Neurosci 6:316–322PubMedCrossRefGoogle Scholar
  39. Haier RJ (2014) Increased intelligence is a myth (so far). Front Syst Neurosci 8:34PubMedPubMedCentralCrossRefGoogle Scholar
  40. Haier RJ, Jung RE, Yeo RA, Head K, Alkire MT (2004) Structural brain variation and general intelligence. Neuroimage 23:425–433PubMedCrossRefGoogle Scholar
  41. Hambrick DZ, Altmann EM (2015) The role of placekeeping ability in fluid intelligence. Psychon Bull Rev 22:1104–1110PubMedCrossRefGoogle Scholar
  42. Hossiep R, Turck D, Hasella M (1999) Bochumer Matrizentest: BOMAT advanced-short version. Hogrefe, GöttingenGoogle Scholar
  43. Houde O, Zago L, Mellet E, Moutier S, Pineau A, Mazoyer B, Tzourio-Mazoyer N (2000) Shifting from the perceptual brain to the logical brain: the neural impact of cognitive inhibition training. J Cogn Neurosci 12:721–728PubMedCrossRefGoogle Scholar
  44. Jacob SN, Nieder A (2014) Complementary roles for primate frontal and parietal cortex in guarding working memory from distractor stimuli. Neuron 83:226–237PubMedCrossRefGoogle Scholar
  45. Jaeggi SM, Buschkuehl M, Jonides J, Perrig WJ (2008) Improving fluid intelligence with training on working memory. Proc Natl Acad Sci USA 105:6829–6833PubMedPubMedCentralCrossRefGoogle Scholar
  46. Jung RE, Haier RJ (2007) The Parieto-Frontal Integration Theory (P-FIT) of intelligence: converging neuroimaging evidence. Behav Brain Sci 30:135–154PubMedCrossRefGoogle Scholar
  47. Kaiser FH (1958) The varimax criterion for analytic rotation in factor analysis. Psychometrika 23:187–200CrossRefGoogle Scholar
  48. Khanna A, Pascual-Leone A, Farzan F (2014) Reliability of resting-state microstate features in electroencephalography. PLoS ONE 9:e114163PubMedPubMedCentralCrossRefGoogle Scholar
  49. Khanna A, Pascual-Leone A, Michel CM, Farzan F (2015) Microstates in resting-state EEG: current status and future directions. Neurosci Biobehav Rev 49:105–113PubMedCrossRefGoogle Scholar
  50. Kievit RA, Davis SW, Mitchell DJ, Taylor JR, Duncan J, Henson RN (2014) Distinct aspects of frontal lobe structure mediate age-related differences in fluid intelligence and multitasking. Nat Commun 5:5658PubMedPubMedCentralCrossRefGoogle Scholar
  51. Koenig T, Lehmann D (1996) Microstates in language-related brain potential maps show noun-verb differences. Brain Lang 53:169–182PubMedCrossRefGoogle Scholar
  52. Koenig T, Melie-Garcia L (2009) Statistical analysis of multichannel scalp field data. In: Koenig T, Melie-Garcia L, Electrical neuroimaging. Cambridge University, Cambridge 53:169–190CrossRefGoogle Scholar
  53. Koenig T, Kochi K, Lehmann D,(1998) Event-related electric microstates of the brain differ between words with visual and abstract meaning. Electroencephalogr Clin Neurophysiol 106:535–546PubMedCrossRefGoogle Scholar
  54. Koenig T, Prichep L, Lehmann D, Sosa PV, Braeker E, Kleinlogel H, Isenhart R, John ER (2002) Millisecond by millisecond, year by year: normative EEG microstates and developmental stages. Neuroimage 16:41–48PubMedCrossRefGoogle Scholar
  55. Kundu B, Sutterer DW, Emrich SM, Postle BR (2013) Strengthened effective connectivity underlies transfer of working memory training to tests of short-term memory and attention. J Neurosci 33:8705–8715PubMedPubMedCentralCrossRefGoogle Scholar
  56. Kush JC, Spring MB, Barkand J (2012) Advances in the assessment of cognitive skills using computer-based measurement. Behav Res Methods 44:125–134PubMedCrossRefGoogle Scholar
  57. Lancaster JL, Woldorff MG, Parsons LM, Liotti M, Freitas CS, Rainey L, Kochunov PV, Nickerson D, Mikiten SA, Fox PT (2000) Automated Talairach atlas labels for functional brain mapping. Hum Brain Mapp 10(3):120–131PubMedCrossRefGoogle Scholar
  58. Langer N, von Bastian CC, Wirz H, Oberauer K, Jancke L (2013) The effects of working memory training on functional brain network efficiency. Cortex 49:2424–2438PubMedCrossRefGoogle Scholar
  59. Lehmann D (1971) Multichannel topography of human alpha EEG fields. Electroencephalogr Clin Neurophysiol 31:439–449PubMedCrossRefGoogle Scholar
  60. Lehmann D, Faber PL, Galderisi S, Herrmann WM, Kinoshita T, Koukkou M, Mucci A, Pascual-Marqui RD, Saito N, Wackermann J, Winterer G, Koenig T (2005) EEG microstate duration and syntax in acute, medication-naive, first-episode schizophrenia: a multi-center study. Psychiatry Res 138:141–156PubMedCrossRefGoogle Scholar
  61. Malhotra P, Coulthard EJ, Husain M (2009) Role of right posterior parietal cortex in maintaining attention to spatial locations over time. Brain 132:645–660PubMedPubMedCentralCrossRefGoogle Scholar
  62. Martinez A, Anllo-Vento L, Sereno MI, Frank LR, Buxton RB, Dubowitz DJ, Wong EC, Hinrichs H, Heinze HJ, Hillyard SA (1999) Involvement of striate and extrastriate visual cortical areas in spatial attention. Nat Neurosci 2:364–369PubMedCrossRefGoogle Scholar
  63. Matsuda O, Saito M (1998) Crystallized and fluid intelligence in elderly patients with mild dementia of the Alzheimer type. Int Psychogeriatr 10:147–154PubMedCrossRefGoogle Scholar
  64. Matzen LE, Benz ZO, Dixon KR, Posey J, Kroger JK, Speed AE (2010) Recreating Raven’s: software for systematically generating large numbers of Raven-like matrix problems with normed properties. Behav Res Methods 42:525–541PubMedCrossRefGoogle Scholar
  65. Mazziotta J, Toga A, Evans A, Fox P, Lancaster J, Zilles K et al (2001) A four-dimensional probabilistic atlas of the human brain. J Am Med Inf Assoc, 8(5):401–430CrossRefGoogle Scholar
  66. Melara RD, Tong Y, Rao A (2012) Control of working memory: effects of attention training on target recognition and distractor salience in an auditory selection task. Brain Res 1430:68–77PubMedCrossRefGoogle Scholar
  67. Melnick MD, Harrison BR, Park S, Bennetto L, Tadin D (2013) A strong interactive link between sensory discriminations and intelligence. Curr Biol 23:1013–1017PubMedPubMedCentralCrossRefGoogle Scholar
  68. Michel CM, Lehmann D (1993) Single doses of piracetam affect 42-channel event-related potential microstate maps in a cognitive paradigm. Neuropsychobiology 28:212–221PubMedCrossRefGoogle Scholar
  69. Micheloyannis S, Pachou E, Stam CJ, Vourkas M, Erimaki S, Tsirka V (2006) Using graph theoretical analysis of multi channel EEG to evaluate the neural efficiency hypothesis. Neurosci Lett 402:273–277PubMedCrossRefGoogle Scholar
  70. Milz P, Faber PL, Lehmann D, Koenig T, Kochi K, Pascual-Marqui RD (2015) The functional significance of EEG microstates-associations with modalities of thinking. Neuroimage 125:643–656PubMedCrossRefGoogle Scholar
  71. Mognon A, Jovicich J, Bruzzone L, Buiatti M (2011) ADJUST: an automatic EEG artifact detector based on the joint use of spatial and temporal features. Psychophysiology 48:229–240PubMedCrossRefGoogle Scholar
  72. Moody S (2009) Can intelligence be increased by training on a task of working memory? Intelligence 7:327–328CrossRefGoogle Scholar
  73. Narr KL, Woods RP, Thompson PM, Szeszko P, Robinson D, Dimtcheva T, Gurbani M, Toga AW, Bilder RM (2007) Relationships between IQ and regional cortical gray matter thickness in healthy adults. Cereb Cortex 17:2163–2171PubMedCrossRefGoogle Scholar
  74. Neubauer AC, Fink A (2009) Intelligence and neural efficiency. Neurosci Biobehav Rev 33:1004–1023Google Scholar
  75. Neubauer AC, Grabner RH, Freudenthaler HH, Beckmann JF, Guthke J (2004) Intelligence and individual differences in becoming neurally efficient. Acta Psychol 116:55–74CrossRefGoogle Scholar
  76. Nichols TE, Holmes AP (2002) Nonparametric permutation tests for functional neuroimaging: a primer with examples. Hum Brain Mapp 15(1):1–25PubMedCrossRefGoogle Scholar
  77. Nishida K, Morishima Y, Yoshimura M, Isotani T, Irisawa S, Jann K, Dierks T, Strik W, Kinoshita T, Koenig T (2013) EEG microstates associated with salience and frontoparietal networks in frontotemporal dementia, schizophrenia and Alzheimer’s disease. Clin Neurophysiol 124(6):1106–1114PubMedCrossRefGoogle Scholar
  78. Oelhafen S, Nikolaidis A, Padovani T, Blaser D, Koenig T, Perrig WJ (2013) Increased parietal activity after training of interference control. Neuropsychologia 51:2781–2790PubMedCrossRefGoogle Scholar
  79. Pahor A, Jausovec N (2014) The effects of theta transcranial alternating current stimulation (tACS) on fluid intelligence. Int J Psychophysiol 93:322–331PubMedCrossRefGoogle Scholar
  80. Pascual-Marqui RD (2007) Discrete, 3D distributed, linear imaging methods of electric neuronal activity. Part 1: exact, zero error localization. arXiv:0710.3341 [Math-Ph, Physics:physics, Q-Bio], OctoberGoogle Scholar
  81. Pascual-Marqui RD, Michel CM, Lehmann D (1995) Segmentation of brain electrical activity into microstates: model estimation and validation. IEEE Trans Biomed Eng 42:658–665CrossRefPubMedGoogle Scholar
  82. Pascual-Marqui RD, Lehmann D, Faber P, Milz P, Kochi K, Yoshimura M, Nishida K, Isotani T, Kinoshita T (2014) The resting microstate networks (RMN): cortical distributions, dynamics, and frequency specific information flow. http://arxiv.org/abs/1411.1949v2
  83. Pereira J, Wang XJ (2015) A tradeoff between accuracy and flexibility in a working memory circuit endowed with slow feedback mechanisms. Cereb Cortex 25:3586–3601PubMedCrossRefGoogle Scholar
  84. Perfetti B, Saggino A, Ferretti A, Caulo M, Romani GL, Onofrj M (2009) Differential patterns of cortical activation as a function of fluid reasoning complexity. Hum Brain Mapp 30:497–510PubMedCrossRefGoogle Scholar
  85. Peters JC, Roelfsema PR, Goebel R (2012) Task-relevant and accessory items in working memory have opposite effects on activity in extrastriate cortex. J Neurosci 32:17003–17011PubMedCrossRefGoogle Scholar
  86. Prado J, Noveck IA (2007) Overcoming perceptual features in logical reasoning: a parametric functional magnetic resonance imaging study. J Cogn Neurosci 19:642–657PubMedCrossRefGoogle Scholar
  87. Prado J, Kaliuzhna M, Cheylus A, Noveck IA (2008) Overcoming perceptual features in logical reasoning: an event-related potentials study. Neuropsychologia 46:2629–2637PubMedCrossRefGoogle Scholar
  88. Prado J, Van Der Henst JB, Noveck IA (2010) Recomposing a fragmented literature: how conditional and relational arguments engage different neural systems for deductive reasoning. Neuroimage 51:1213–1221PubMedCrossRefGoogle Scholar
  89. Raven J, Raven JC, Court JH (2003) Manual for Raven’s progressive matrices and vocabulary scales. Section 1: general overview. Harcourt Assessment, San AntoniaGoogle Scholar
  90. Raz N, Lindenberger U, Ghisletta P, Rodrigue KM, Kennedy KM, Acker JD (2008) Neuroanatomical correlates of fluid intelligence in healthy adults and persons with vascular risk factors. Cereb Cortex 18:718–726PubMedCrossRefGoogle Scholar
  91. Rushton JP, Ankney CD (2009) Whole brain size and general mental ability: a review. Int J Neurosci 119:691–731PubMedCrossRefGoogle Scholar
  92. Santarnecchi E, Rossi S (2016b) Advances in the neuroscience of intelligence: from brain connectivity to brain perturbation. Span J Psychol. doi:10.1017/sjp.2016.89 PubMedGoogle Scholar
  93. Santarnecchi E, Polizzotto NR, Godone M, Giovannelli F, Feurra M, Matzen L, Rossi A, Rossi S (2013) Frequency-dependent enhancement of fluid intelligence induced by transcranial oscillatory potentials. Curr Biol 23:1449–1453PubMedCrossRefGoogle Scholar
  94. Santarnecchi E, Galli G, Polizzotto NR, Rossi A, Rossi S (2014) Efficiency of weak brain connections support general cognitive functioning. Hum Brain Mapp 35:4566–4582PubMedCrossRefGoogle Scholar
  95. Santarnecchi E, Brem AK, Levenbaum E, Thompson T, Kadosh RC, Pascual-Leone A (2015a) Enhancing cognition using transcranial electrical stimulation. Curr Opin Behav Sci 4:171–178CrossRefGoogle Scholar
  96. Santarnecchi E, Rossi S, Rossi A (2015b) The smarter, the stronger: intelligence level correlates with brain resilience to systematic insults. Cortex 64:293–309PubMedCrossRefGoogle Scholar
  97. Santarnecchi E, Tatti E, Rossi S, Serino V, Rossi A (2015c) Intelligence-related differences in the asymmetry of spontaneous cerebral activity. Hum Brain Mapp 36:3586–3602PubMedCrossRefGoogle Scholar
  98. Santarnecchi E, Muller T, Rossi S, Sarkar A, Polizzotto NR, Rossi A, Kadosh RC (2016a) Individual differences and specificity of prefrontal gamma frequency-tACS on fluid intelligence capabilities. Cortex 23:1449–1453Google Scholar
  99. Sitaram R, Ros T, Stoeckel L, Haller S, Scharnowski F, Lewis-Peacock J, Weiskopf N et al (2016) Closed-loop brain training: the science of neurofeedback. Nat Rev Neurosci 18(2):86–100PubMedCrossRefGoogle Scholar
  100. Soulieres I, Dawson M, Samson F, Barbeau EB, Sahyoun CP, Strangman GE, Zeffiro TA, Mottron L (2009) Enhanced visual processing contributes to matrix reasoning in autism. Hum Brain Mapp 30:4082–4107PubMedPubMedCentralCrossRefGoogle Scholar
  101. Stern Y (2002) What is cognitive reserve? Theory and research application of the reserve concept. J Int Neuropsychol Soc 8:448–460PubMedCrossRefGoogle Scholar
  102. Stern Y (2009) Cognitive reserve. Neuropsychologia 47:2015–2028PubMedPubMedCentralCrossRefGoogle Scholar
  103. Thompson TW, Waskom ML, Garel KL, Cardenas-Iniguez C, Reynolds GO, Winter R, Chang P, Pollard K, Lala N, Alvarez GA, Gabrieli JD (2013) Failure of working memory training to enhance cognition or intelligence. PLoS ONE 8:e63614PubMedPubMedCentralCrossRefGoogle Scholar
  104. Tibshirania R, Walther G (2005) Cluster validation by prediction strength. J Comput Graph Stat 14:511–528CrossRefGoogle Scholar
  105. Tomescu MI, Rihs TA, Becker R, Britz J, Custo A, Grouiller F, Schneider M, Debbane M, Eliez S, Michel CM (2014) Deviant dynamics of EEG resting state pattern in 22q11.2 deletion syndrome adolescents: a vulnerability marker of schizophrenia? Schizophr Res 157:175–181PubMedCrossRefGoogle Scholar
  106. Vakhtin AA, Ryman SG, Flores RA, Jung RE (2014) Functional brain networks contributing to the Parieto-Frontal Integration Theory of Intelligence. Neuroimage 103:349–354PubMedCrossRefGoogle Scholar
  107. Van De Ville D, Britz J, Michel CM (2010) EEG microstate sequences in healthy humans at rest reveal scale-free dynamics. Proc Natl Acad Sci USA 107:18179–18184PubMedCrossRefGoogle Scholar
  108. van den Heuvel MP, Stam CJ, Kahn RS, Hulshoff Pol HE (2009) Efficiency of functional brain networks and intellectual performance. J Neurosci 29:7619–7624PubMedCrossRefGoogle Scholar
  109. Verney SP, Granholm E, Marshall SP, Malcarne VL, Saccuzzo DP (2005) Culture-fair cognitive ability assessment: information processing and psychophysiological approaches. Assessment 12:303–319PubMedCrossRefGoogle Scholar
  110. Wartenburger I, Heekeren HR, Preusse F, Kramer J, van der Meer E (2009) Cerebral correlates of analogical processing and their modulation by training. Neuroimage 48:291–302PubMedCrossRefGoogle Scholar
  111. Whalley LJ, Deary IJ, Appleton CL, Starr JM (2004) Cognitive reserve and the neurobiology of cognitive aging. Ageing Res Rev 3:369–382PubMedCrossRefGoogle Scholar
  112. Yuan Z, Qin W, Wang D, Jiang T, Zhang Y, Yu C (2012) The salience network contributes to an individual’s fluid reasoning capacity. Behav Brain Res 229:384–390PubMedCrossRefGoogle Scholar
  113. Zhang G, Yao L, Shen J, Yang Y, Zhao X (2015) Reorganization of functional brain networks mediates the improvement of cognitive performance following real-time neurofeedback training of working memory: brain networks mediate post-training behavior. Hum Brain Mapp 36(5):1705–1715PubMedCrossRefGoogle Scholar
  114. Zook NA, Davalos DB, Delosh EL, Davis HP (2004) Working memory, inhibition, and fluid intelligence as predictors of performance on Tower of Hanoi and London tasks. Brain Cogn 56:286–292PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  • Emiliano Santarnecchi
    • 1
    • 2
    • 3
  • Arjun R. Khanna
    • 1
  • Christian S. Musaeus
    • 1
    • 4
  • Christopher S. Y. Benwell
    • 1
    • 5
  • Paula Davila
    • 1
  • Faranak Farzan
    • 6
  • Santosh Matham
    • 7
  • Alvaro Pascual-Leone
    • 1
    • 8
  • Mouhsin M. Shafi
    • 1
  • on behalf of Honeywell SHARP Team authors
  1. 1.Berenson-Allen Center for Non-Invasive Brain Stimulation, Division of Cognitive Neurology, Beth Israel Medical CenterHarvard Medical SchoolBostonUSA
  2. 2.Siena-Brain Investigation & Neuromodulation Lab (SiBIN), Department of Medicine, Surgery and Neuroscience, Neurology and Clinical Neurophysiology SectionUniversity of SienaSienaItaly
  3. 3.Siena Robotics and Systems Lab (SIRS-Lab), Engineering and Mathematics DepartmentUniversity of SienaSienaItaly
  4. 4.Department of Neurology, Danish Dementia Research Centre (DDRC), RigshospitaletUniversity of CopenhagenCopenhagenDenmark
  5. 5.Centre for Cognitive Neuroimaging, Institute of Neuroscience & PsychologyUniversity of GlasgowGlasgowUK
  6. 6.Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental HealthUniversity of TorontoTorontoCanada
  7. 7.Honeywell LabsHoneywell AerospaceRedmondUSA
  8. 8.Institut Universitari de Neurorehabilitacio GuttmannBadalonaSpain

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