AGE

, 36:9643

Synchronization during an internally directed cognitive state in healthy aging and mild cognitive impairment: a MEG study

  • María Eugenia López
  • Pilar Garcés
  • Pablo Cuesta
  • Nazareth P. Castellanos
  • Sara Aurtenetxe
  • Ricardo Bajo
  • Alberto Marcos
  • Mercedes Montenegro
  • Raquel Yubero
  • Francisco del Pozo
  • Miguel Sancho
  • Fernando Maestú
Article

Abstract

Mild cognitive impairment (MCI) is a stage between healthy aging and dementia. It is known that in this condition the connectivity patterns are altered in the resting state and during cognitive tasks, where an extra effort seems to be necessary to overcome cognitive decline. We aimed to determine the functional connectivity pattern required to deal with an internally directed cognitive state (IDICS) in healthy aging and MCI. This task differs from the most commonly employed ones in neurophysiology, since inhibition from external stimuli is needed, allowing the study of this control mechanism. To this end, magnetoencephalographic (MEG) signals were acquired from 32 healthy individuals and 38 MCI patients, both in resting state and while performing a subtraction task of two levels of difficulty. Functional connectivity was assessed with phase locking value (PLV) in five frequency bands. Compared to controls, MCIs showed higher PLV values in delta, theta, and gamma bands and an opposite pattern in alpha, beta, and gamma bands in resting state. These changes were associated with poorer neuropsychological performance. During the task, this group exhibited a hypersynchronization in delta, theta, beta, and gamma bands, which was also related to a lower cognitive performance, suggesting an abnormal functioning in this group. Contrary to controls, MCIs presented a lack of synchronization in the alpha band which may denote an inhibition deficit. Additionally, the magnitude of connectivity changes rose with the task difficulty in controls but not in MCIs, in line with the compensation-related utilization of neural circuits hypothesis (CRUNCH) model.

Keywords

Mild cognitive impairment Internally directed cognitive state (IDICS) Functional connectivity MEG CRUNCH 

References

  1. Agrell B, Dehlin O (1998) The clock-drawing test. Age Ageing 27:399–403. doi:10.1093/ageing/27.3.399 CrossRefGoogle Scholar
  2. Albert MS, DeKosky ST, Dickson D et al (2011) The diagnosis of mild cognitive impairment due to Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7:270–279. doi:10.1016/j.jalz.2011.03.008 PubMedCentralPubMedCrossRefGoogle Scholar
  3. Andrews-Hanna JR (2012) The brain’s default network and its adaptive role in internal mentation. Neuroscientist 18:251–270. doi:10.1177/1073858411403316 PubMedCentralPubMedCrossRefGoogle Scholar
  4. Auer S, Reisberg B (1997) The GDS/FAST staging system. Int Psychogeriatr 9(Suppl 1):167–171PubMedCrossRefGoogle Scholar
  5. Aurtenetxe S, Castellanos NP, Moratti S et al (2013) Dysfunctional and compensatory duality in mild cognitive impairment during a continuous recognition memory task. Int J Psychophysiol 87:95–102. doi:10.1016/j.ijpsycho.2012.11.008 PubMedCrossRefGoogle Scholar
  6. Bajo R, Maestú F, Nevado A et al (2010) Functional connectivity in mild cognitive impairment during a memory task: implications for the disconnection hypothesis. J Alzheimers Dis 22:183–193. doi:10.3233/JAD-2010-100177 PubMedGoogle Scholar
  7. Bajo R, Castellanos NP, Cuesta P et al (2012) Differential patterns of connectivity in progressive mild cognitive impairment. Brain Connect 2:21–24. doi:10.1089/brain.2011.0069 PubMedCrossRefGoogle Scholar
  8. Benedek M, Bergner S, Könen T et al (2011) EEG alpha synchronization is related to top-down processing in convergent and divergent thinking. Neuropsychologia 49:3505–3511. doi:10.1016/j.neuropsychologia.2011.09.004 PubMedCentralPubMedCrossRefGoogle Scholar
  9. Benton A, Hamsher K (1989) Multilingual aplasia examination, 2nd edn. AJA Associates, Iowa CityGoogle Scholar
  10. Berendse H, Verbunt JP, Scheltens P et al (2000) Magnetoencephalographic analysis of cortical activity in Alzheimer’s disease: a pilot study. Clin Neurophysiol 111:604–612. doi:10.1016/S1388-2457(99)00309-0 PubMedCrossRefGoogle Scholar
  11. Braak H, Braak E (1991) Neuropathological stageing of Alzheimer-related changes. Acta Neuropathol 82:239–259PubMedCrossRefGoogle Scholar
  12. Buldú JM, Bajo R, Maestú F et al (2011) Reorganization of functional networks in mild cognitive impairment. PLoS One 6:e19584. doi:10.1371/journal.pone.0019584 PubMedCentralPubMedCrossRefGoogle Scholar
  13. Chételat G, Desgranges B, De La Sayette V et al (2002) Mapping gray matter loss with voxel-based morphometry in mild cognitive impairment. Neuroreport 13:1939–1943PubMedCrossRefGoogle Scholar
  14. Cirrito JR, Kang J-E, Lee J et al (2008) Endocytosis is required for synaptic activity-dependent release of amyloid-beta in vivo. Neuron 58:42–51. doi:10.1016/j.neuron.2008.02.003 PubMedCentralPubMedCrossRefGoogle Scholar
  15. Cooper NR, Croft RJ, Dominey SJJ et al (2003) Paradox lost? Exploring the role of alpha oscillations during externally vs. internally directed attention and the implications for idling and inhibition hypotheses. Int J Psychophysiol 47:65–74PubMedCrossRefGoogle Scholar
  16. De Haan W, Mott K, van Straaten ECW et al (2012) Activity dependent degeneration explains hub vulnerability in Alzheimer’s disease. PLoS Comput Biol 8:e1002582. doi:10.1371/journal.pcbi.1002582 PubMedCentralPubMedCrossRefGoogle Scholar
  17. Delbeuck X, Van der Linden M, Collette F (2003) Alzheimer’s disease as a disconnection syndrome? Neuropsychol Rev 13:79–92PubMedCrossRefGoogle Scholar
  18. Dimitriadis SI, Laskaris NA, Tsirka V et al (2010) What does delta band tell us about cognitive processes: a mental calculation study. Neurosci Lett 483:11–15. doi:10.1016/j.neulet.2010.07.034 PubMedCrossRefGoogle Scholar
  19. Dubois B, Feldman HH, Jacova C et al (2007) Research criteria for the diagnosis of Alzheimer’s disease: revising the NINCDS-ADRDA criteria. Lancet Neurol 6:734–746. doi:10.1016/S1474-4422(07)70178-3 PubMedCrossRefGoogle Scholar
  20. Dubois B, Feldman HH, Jacova C et al (2010) Revising the definition of Alzheimer’s disease: a new lexicon. Lancet Neurol 9:1118–1127. doi:10.1016/S1474-4422(10)70223-4 PubMedCrossRefGoogle Scholar
  21. Fellgiebel A, Müller MJ, Wille P et al (2005) Color-coded diffusion-tensor-imaging of posterior cingulate fiber tracts in mild cognitive impairment. Neurobiol Aging 26:1193–1198. doi:10.1016/j.neurobiolaging.2004.11.006 PubMedCrossRefGoogle Scholar
  22. Fernández A, Hornero R, Mayo A et al (2006) MEG spectral profile in Alzheimer’s disease and mild cognitive impairment. Clin Neurophysiol 117:306–314. doi:10.1016/j.clinph.2005.10.017 PubMedCrossRefGoogle Scholar
  23. Fischl B, Salat DH, Busa E et al (2002) Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neuron 33:341–355PubMedCrossRefGoogle Scholar
  24. Friston KJ (2001) Brain function, nonlinear coupling, and neuronal transients. Neuroscientist 7:406–418PubMedCrossRefGoogle Scholar
  25. Fujie S, Namiki C, Nishi H et al (2008) The role of the uncinate fasciculus in memory and emotional recognition in amnestic mild cognitive impairment. Dement Geriatr Cogn Disord 26:432–439. doi:10.1159/000165381 PubMedCrossRefGoogle Scholar
  26. Garcia-Marin V, Blazquez-Llorca L, Rodriguez J-R et al (2009) Diminished perisomatic GABAergic terminals on cortical neurons adjacent to amyloid plaques. Front Neuroanat 3:28. doi:10.3389/neuro.05.028.2009 PubMedCentralPubMedCrossRefGoogle Scholar
  27. Gauthier S, Reisberg B, Zaudig M et al (2006) Mild cognitive impairment. Lancet 1262–1270Google Scholar
  28. Gevins A, Smith ME, McEvoy L, Yu D (1997) High-resolution EEG mapping of cortical activation related to working memory: effects of task difficulty, type of processing, and practice. Cereb Cortex 7:374–385PubMedCrossRefGoogle Scholar
  29. Giannitrapani D (1971) Scanning mechanisms and the EEG. Electroencephalogr Clin Neurophysiol 30:139–146PubMedCrossRefGoogle Scholar
  30. Gómez C, Stam CJ, Hornero R et al (2009) Disturbed beta band functional connectivity in patients with mild cognitive impairment: an MEG study. IEEE Trans Biomed Eng 56:1683–1690. doi:10.1109/TBME.2009.2018454 PubMedCrossRefGoogle Scholar
  31. Grundman M, Petersen RC, Ferris SH et al (2004) Mild cognitive impairment can be distinguished from Alzheimer disease and normal aging for clinical trials. Arch Neurol 61:59–66. doi:10.1001/archneur.61.1.59 PubMedCrossRefGoogle Scholar
  32. Haense C, Kalbe E, Herholz K et al (2012) Cholinergic system function and cognition in mild cognitive impairment. Neurobiol Aging 33:867–877. doi:10.1016/j.neurobiolaging.2010.08.015 PubMedCrossRefGoogle Scholar
  33. Hämäläinen M, Hari R, Ilmoniemi RJ et al (1993) Magnetoencephalography—theory, instrumentation, and applications to noninvasive studies of the working human brain. Rev Mod Phys 65:413–497. doi:10.1103/RevModPhys.65.413 CrossRefGoogle Scholar
  34. Harmony T, Fernández T, Silva J et al (1996) EEG delta activity: an indicator of attention to internal processing during performance of mental tasks. Int J Psychophysiol 24:161–171PubMedCrossRefGoogle Scholar
  35. Harmony T, Fernández T, Gersenowies J, Galán L, Fernández-Bouzas A, Aubert E, Díaz-Comas L (2004) Specific EEG frequencies signal general common cognitive processes as well as specific task processes in man. International Journal of Psychophysiology : Official Journal of the International Organization of Psychophysiology 53(3):207–16. doi:10.1016/j.ijpsycho.2004.04.006 Google Scholar
  36. He J, Farias S, Martinez O et al (2009) Differences in brain volume, hippocampal volume, cerebrovascular risk factors, and apolipoprotein E4 among mild cognitive impairment subtypes. Arch Neurol 66:1393–1399. doi:10.1001/archneurol.2009.252 PubMedCentralPubMedGoogle Scholar
  37. Ishii R, Shinosaki K, Ukai S et al (1999) Medial prefrontal cortex generates frontal midline theta rhythm. Neuroreport 10:675–679PubMedCrossRefGoogle Scholar
  38. Jelic V, Johansson S-E, Almkvist O et al (2000) Quantitative electroencephalography in mild cognitive impairment: longitudinal changes and possible prediction of Alzheimer’s disease. Neurobiol Aging 21:533–540. doi:10.1016/S0197-4580(00)00153-6 PubMedCrossRefGoogle Scholar
  39. Jensen O, Mazaheri A (2010) Shaping functional architecture by oscillatory alpha activity: gating by inhibition. Front Hum Neurosci 4:186. doi:10.3389/fnhum.2010.00186 PubMedCentralPubMedCrossRefGoogle Scholar
  40. Jensen O, Gelfand J, Kounios J, Lisman JE (2002) Oscillations in the alpha band (9–12 Hz) increase with memory load during retention in a short-term memory task. Cereb Cortex 12:877–882PubMedCrossRefGoogle Scholar
  41. Jeong J (2004) EEG dynamics in patients with Alzheimer’s disease. Clin Neurophysiol 115:1490–1505. doi:10.1016/j.clinph.2004.01.001 PubMedCrossRefGoogle Scholar
  42. Jiang Z (2005) Study on EEG power and coherence in patients with mild cognitive impairment during working memory task. J Zhejiang Univ Sci B 6:1213–1219. doi:10.1631/jzus.2005.B1213 PubMedCentralPubMedCrossRefGoogle Scholar
  43. Jiang Z, Zheng L (2006) Inter- and intra-hemispheric EEG coherence in patients with mild cognitive impairment at rest and during working memory task. J Zhejiang Univ Sci B 7:357–364. doi:10.1631/jzus.2006.B0357 PubMedCentralPubMedCrossRefGoogle Scholar
  44. Jiang Z, Zheng L, Yu E-Y (2008) EEG coherence characteristics at rest and during a three-level working memory task in normal aging and mild cognitive impairment. Med Sci Monit 14:515–524Google Scholar
  45. Kaplan E, Goodglass H, Weintraub S (1983) The Boston Naming Test. Lea and Febiger, PhiladelphiaGoogle Scholar
  46. Klimesch W, Sauseng P, Hanslmayr S (2007) EEG alpha oscillations: the inhibition-timing hypothesis. Brain Res Rev 53:63–88. doi:10.1016/j.brainresrev.2006.06.003 PubMedCrossRefGoogle Scholar
  47. Koenig T, Prichep L, Dierks T et al (2005) Decreased EEG synchronization in Alzheimer’s disease and mild cognitive impairment. Neurobiol Aging 26:165–171. doi:10.1016/j.neurobiolaging.2004.03.008 PubMedCrossRefGoogle Scholar
  48. Krueger F, Landgraf S, van der Meer E et al (2011) Effective connectivity of the multiplication network: a functional MRI and multivariate Granger Causality Mapping study. Hum Brain Mapp 32:1419–1431. doi:10.1002/hbm.21119 PubMedCrossRefGoogle Scholar
  49. Lawton MP, Brody EM (1969) Assessment of older people: self-maintaining and instrumental activities of daily living. Gerontologist 9:179–186PubMedCrossRefGoogle Scholar
  50. Leirer VM, Wienbruch C, Kolassa S et al (2011) Changes in cortical slow wave activity in healthy aging. Brain Imaging Behav 5:222–228. doi:10.1007/s11682-011-9126-3 PubMedCrossRefGoogle Scholar
  51. Li X, Zhang Y, Feng L, Meng Q (2010) Early event-related potentials changes during simple mental calculation in Chinese older adults with mild cognitive impairment: a case-control study. Neurosci Lett 475:29–32. doi:10.1016/j.neulet.2010.03.038 PubMedCrossRefGoogle Scholar
  52. Lobo A, Ezquerra J, Gómez Burgada F et al (1979) Cognocitive mini-test (a simple practical test to detect intellectual changes in medical patients). Actas Luso Esp Neurol Psiquiatr Cienc Afines 7:189–202PubMedGoogle Scholar
  53. Locatelli T, Cursi M, Liberati D, Franceschi M, Comi G (1998) EEG coherence in Alzheimer’s disease. Electroen Clin Neuro 106(3):229–37. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/9743281 Google Scholar
  54. Maris E, Oostenveld R (2007) Nonparametric statistical testing of EEG- and MEG-data. J Neurosci Methods 164:177–190. doi:10.1016/j.jneumeth.2007.03.024 PubMedCrossRefGoogle Scholar
  55. McKhann G, Drachman D, Folstein M et al (1984) Clinical diagnosis of Alzheimer’s disease: report of the NINCDS-ADRDA Work Group under the auspices of Department of Health and Human Services Task Force on Alzheimer’s Disease. Neurology 34:939–944PubMedCrossRefGoogle Scholar
  56. Micheloyannis S, Sakkalis V, Vourkas M et al (2005) Neural networks involved in mathematical thinking: evidence from linear and non-linear analysis of electroencephalographic activity. Neurosci Lett 373:212–217. doi:10.1016/j.neulet.2004.10.005 PubMedCrossRefGoogle Scholar
  57. Molinuevo JL, Rami L (2013) Applying the IWG research criteria in clinical practice: feasibility and ethical issues. Med Clin North Am 97:477–484. doi:10.1016/j.mcna.2012.12.018 PubMedCrossRefGoogle Scholar
  58. Morcom AM, Li J, Rugg MD (2007) Age effects on the neural correlates of episodic retrieval: increased cortical recruitment with matched performance. Cereb Cortex 17:2491–2506. doi:10.1093/cercor/bhl155 PubMedCrossRefGoogle Scholar
  59. Moretti DV, Miniussi C, Frisoni GB et al (2007) Hippocampal atrophy and EEG markers in subjects with mild cognitive impairment. Clin Neurophysiol 118:2716–2729. doi:10.1016/j.clinph.2007.09.059 PubMedCrossRefGoogle Scholar
  60. Moretti DV, Frisoni GB, Pievani M et al (2008) Cerebrovascular disease and hippocampal atrophy are differently linked to functional coupling of brain areas: an EEG coherence study in MCI subjects. J Alzheimers Dis 14:285–299PubMedGoogle Scholar
  61. Mormann F, Lehnertz K, David P, Elger CE (2000) Mean phase coherence as a measure for phase synchronization and its application to the EEG of epilepsy patients. Phys D Nonlinear Phenom 144:358–369. doi:10.1016/S0167-2789(00)00087-7 CrossRefGoogle Scholar
  62. Mufson EJ, Chen EY, Cochran EJ et al (1999) Entorhinal cortex beta-amyloid load in individuals with mild cognitive impairment. Exp Neurol 158:469–490. doi:10.1006/exnr.1999.7086 PubMedCrossRefGoogle Scholar
  63. Norris G, Tate RL (2000) The Behavioural Assessment of the Dysexecutive Syndrome (BADS): ecological, concurrent and construct validity. Neuropsychol Rehabil 10:33–45. doi:10.1080/096020100389282 CrossRefGoogle Scholar
  64. Oldfield RC (1971) The assessment and analysis of handedness: the Edinburgh inventory. Neuropsychologia 9:97–113PubMedCrossRefGoogle Scholar
  65. Onton J, Delorme A, Makeig S (2005) Frontal midline EEG dynamics during working memory. Neuroimage 27:341–356. doi:10.1016/j.neuroimage.2005.04.014 PubMedCrossRefGoogle Scholar
  66. Oostenveld R, Fries P, Maris E, Schoffelen J-M (2011) FieldTrip: open source software for advanced analysis of MEG, EEG, and invasive electrophysiological data. Comput Intell Neurosci 2011:156869. doi:10.1155/2011/156869 PubMedCentralPubMedCrossRefGoogle Scholar
  67. Osipova D, Rantanen K, Ahveninen J et al (2006) Source estimation of spontaneous MEG oscillations in mild cognitive impairment. Neurosci Lett 405:57–61. doi:10.1016/j.neulet.2006.06.045 PubMedCrossRefGoogle Scholar
  68. Palva S, Palva JM (2007) New vistas for alpha-frequency band oscillations. Trends Neurosci 30:150–158. doi:10.1016/j.tins.2007.02.001 PubMedCrossRefGoogle Scholar
  69. Palva JM, Monto S, Kulashekhar S, Palva S (2010) Neuronal synchrony reveals working memory networks and predicts individual memory capacity. Proc Natl Acad Sci U S A 107:7580–7585. doi:10.1073/pnas.0913113107 PubMedCentralPubMedCrossRefGoogle Scholar
  70. Park JY, Lee KS, An SK et al (2012) Gamma oscillatory activity in relation to memory ability in older adults. Int J Psychophysiol 86:58–65. doi:10.1016/j.ijpsycho.2012.08.002 PubMedCrossRefGoogle Scholar
  71. Parlato V, Lopez OL, Panisset M et al (1992) Mental calculation in mild Alzheimer’s disease: a pilot study. Int J Geriatr Psychiatry 7:599–602. doi:10.1002/gps.930070810 CrossRefGoogle Scholar
  72. Peña-Casanova J (1990) Programa Integrado de Exploración Neuropsicológica- Test Barcelona. Protocolo, MassonGoogle Scholar
  73. Petersen RC (2004) Mild cognitive impairment as a diagnostic entity. J Intern Med 256:183–194. doi:10.1111/j.1365-2796.2004.01388.x PubMedCrossRefGoogle Scholar
  74. Petersen RC, Doody R, Kurz A et al (2001) Current concepts in mild cognitive impairment. Arch Neurol 58:1985–1992PubMedCrossRefGoogle Scholar
  75. Petersen RC, Parisi JE, Dickson DW et al (2006) Neuropathologic features of amnestic mild cognitive impairment. Arch Neurol 63:665–672. doi:10.1001/archneur.63.5.665 PubMedCrossRefGoogle Scholar
  76. Pfeffer RI, Kurosaki TT, Harrah CH et al (1982) Measurement of functional activities in older adults in the community. J Gerontol 37:323–329PubMedCrossRefGoogle Scholar
  77. Raichle ME, MacLeod AM, Snyder AZ et al (2001) A default mode of brain function. Proc Natl Acad Sci U S A 98:676–682. doi:10.1073/pnas.98.2.676 PubMedCentralPubMedCrossRefGoogle Scholar
  78. Reisberg B, Ferris SH, de Leon MJ, Crook T (1982) The Global Deterioration Scale for assessment of primary degenerative dementia. Am J Psychiatry 139:1136–1139PubMedGoogle Scholar
  79. Reitan R (1958) Validity of the Trail Making test as an indicator of organic brain damage. Percept Mot Ski 8:271–276CrossRefGoogle Scholar
  80. Rémy F, Mirrashed F, Campbell B, Richter W (2004) Mental calculation impairment in Alzheimer’s disease : a functional magnetic resonance imaging study. Neurosci Lett 358:25–28. doi:10.1016/j.neulet.2003.12.122 PubMedCrossRefGoogle Scholar
  81. Reuter-Lorenz PA, Cappell KA (2008) Neurocognitive aging and the compensation hypothesis. Curr Dir Psychol Sci 17:177–182. doi:10.1111/j.1467-8721.2008.00570.x CrossRefGoogle Scholar
  82. Rosen WG, Terry RD, Fuld PA et al (1980) Pathological verification of ischemic score in differentiation of dementias. Ann Neurol 7:486–488. doi:10.1002/ana.410070516 PubMedCrossRefGoogle Scholar
  83. Rossini PM, Del Percio C, Pasqualetti P et al (2006) Conversion from mild cognitive impairment to Alzheimer’s disease is predicted by sources and coherence of brain electroencephalography rhythms. Neuroscience 143:793–803. doi:10.1016/j.neuroscience.2006.08.049 PubMedCrossRefGoogle Scholar
  84. Rypma B, Eldreth DA, Rebbechi D (2007) Age-related differences in activation-performance relations in delayed-response tasks: a multiple component analysis. Cortex 43:65–76PubMedCrossRefGoogle Scholar
  85. Sanz-Arigita EJ, Schoonheim MM, Damoiseaux JS et al (2010) Loss of “small-world” networks in Alzheimer’s disease: graph analysis of FMRI resting-state functional connectivity. PLoS One 5:e13788. doi:10.1371/journal.pone.0013788 PubMedCentralPubMedCrossRefGoogle Scholar
  86. Sasaki K, Tsujimoto T, Nishikawa S et al (1996) Frontal mental theta wave recorded simultaneously with magnetoencephalography and electroencephalography. Neurosci Res 26:79–81PubMedCrossRefGoogle Scholar
  87. Sauseng P, Klimesch W, Doppelmayr M et al (2005) EEG alpha synchronization and functional coupling during top-down processing in a working memory task. Hum Brain Mapp 26:148–155. doi:10.1002/hbm.20150 PubMedCrossRefGoogle Scholar
  88. Stam CJ (2010) Use of magnetoencephalography (MEG) to study functional brain networks in neurodegenerative disorders. J Neurol Sci 289:128–134. doi:10.1016/j.jns.2009.08.028 PubMedCrossRefGoogle Scholar
  89. Stam CJ, van Dijk BW (2002) Synchronization likelihood: an unbiased measure of generalized synchronization in multivariate data sets. Physica D: Nonlinear Phenomena 163(3–4):236–251. doi:10.1016/S0167-2789(01)00386-4
  90. Stam CJ, van der Made Y, Pijnenburg YAL, Scheltens P (2003) EEG synchronization in mild cognitive impairment and Alzheimer’s disease. Acta Neurol Scand 108:90–96PubMedCrossRefGoogle Scholar
  91. Stam CJ, Jones BF, Manshanden I et al (2006) Magnetoencephalographic evaluation of resting-state functional connectivity in Alzheimer’s disease. Neuroimage 32:1335–1344. doi:10.1016/j.neuroimage.2006.05.033 PubMedCrossRefGoogle Scholar
  92. Tao H-Y, Tian X (2005) Coherence characteristics of gamma-band EEG during rest and cognitive task in MCI and AD. Conf Proc IEEE Eng Med Biol Soc 3:2747–2750. doi:10.1109/IEMBS.2005.1617040 PubMedGoogle Scholar
  93. Taulu S, Kajola M (2005) Presentation of electromagnetic multichannel data: the signal space separation method. J Appl Phys 97:124905. doi:10.1063/1.1935742 CrossRefGoogle Scholar
  94. v d Pijnenburg YA, Made Y, van Cappellen van Walsum AM et al (2004) EEG synchronization likelihood in mild cognitive impairment and Alzheimer’s disease during a working memory task. Clin Neurophysiol 115:1332–1339. doi:10.1016/j.clinph.2003.12.029 PubMedCrossRefGoogle Scholar
  95. Warrington E, James M (1991) The visual object and space perception battery. Thames Valley Test Company, Bury St. EdmundsGoogle Scholar
  96. Wechsler D (1987) Wechsler memory scale-revised (manual). The Psycho, San AntonioGoogle Scholar
  97. Yener GG, Kurt P, Emek-Savaş DD et al (2013) Reduced visual event-related delta oscillatory responses in amnestic mild cognitive impairment. J Alzheimers Dis 37:759–767. doi:10.3233/JAD-130569 PubMedGoogle Scholar
  98. Yesavage JA, Brink TL, Rose TL et al (1982) Development and validation of a geriatric depression screening scale: a preliminary report. J Psychiatr Res 17:37–49PubMedCrossRefGoogle Scholar
  99. Zamarian L, Semenza C, Domahs F et al (2007a) Alzheimer’s disease and mild cognitive impairment: effects of shifting and interference in simple arithmetic. J Neurol Sci 263:79–88. doi:10.1016/j.jns.2007.06.005 PubMedCrossRefGoogle Scholar
  100. Zamarian L, Stadelmann E, Nürk H-C et al (2007b) Effects of age and mild cognitive impairment on direct and indirect access to arithmetic knowledge. Neuropsychologia 45:1511–1521. doi:10.1016/j.neuropsychologia.2006.11.012 PubMedCrossRefGoogle Scholar
  101. Zamrini E, Maestu F, Pekkonen E et al (2011) Magnetoencephalography as a putative biomarker for Alzheimer’s disease. Int J Alzheimers Dis 2011:280289. doi:10.4061/2011/280289 PubMedCentralPubMedGoogle Scholar
  102. Zarahn E, Rakitin B, Abela D et al (2007) Age-related changes in brain activation during a delayed item recognition task. Neurobiol Aging 28:784–798. doi:10.1016/j.neurobiolaging.2006.03.002 PubMedCrossRefGoogle Scholar
  103. Zhang H, Sachdev PS, Wen W et al (2012) Gray matter atrophy patterns of mild cognitive impairment subtypes. J Neurol Sci 315:26–32. doi:10.1016/j.jns.2011.12.011 PubMedCrossRefGoogle Scholar
  104. Zheng L, Jiang Z, Yu E (2007) Alpha spectral power and coherence in the patients with mild cognitive impairment during a three-level working memory task. J Zhejiang Univ Sci B 8:584–592. doi:10.1631/jzus.2007.B0584 PubMedCentralPubMedCrossRefGoogle Scholar

Copyright information

© American Aging Association 2014

Authors and Affiliations

  • María Eugenia López
    • 1
    • 2
  • Pilar Garcés
    • 1
    • 3
    • 9
  • Pablo Cuesta
    • 1
  • Nazareth P. Castellanos
    • 1
  • Sara Aurtenetxe
    • 1
    • 2
  • Ricardo Bajo
    • 1
    • 4
  • Alberto Marcos
    • 5
  • Mercedes Montenegro
    • 6
  • Raquel Yubero
    • 7
  • Francisco del Pozo
    • 8
  • Miguel Sancho
    • 9
  • Fernando Maestú
    • 1
    • 2
  1. 1.Laboratory of Cognitive and Computational Neuroscience (UCM-UPM)Centre for Biomedical Technology (CTB)MadridSpain
  2. 2.Department of Basic Psychology IIComplutense University of MadridMadridSpain
  3. 3.CEI Campus MoncloaUCM-UPMMadridSpain
  4. 4.Department of MathematicsUniversidad Internacional de La Rioja (UNIR)LogroñoSpain
  5. 5.Neurology DepartmentSan Carlos University HospitalMadridSpain
  6. 6.Memory Decline Prevention CenterMadridSpain
  7. 7.Geriatric DepartmentSan Carlos University HospitalMadridSpain
  8. 8.Centre for Biomedical Technology (CTB)MadridSpain
  9. 9.Departamento de Física Aplicada III, Facultad de FísicaComplutense University of MadridMadridSpain

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