Electroencephalography (EEG) systems have been used for assessing cognitive function in dementia for several decades. Studies have demonstrated that EEG in Alzheimer's disease (AD) patients is generally characterized by significant and specific increases in delta and theta power, a decrease in alpha power, and a decrease in the coherence of the fast bands between different brain areas linked by long corticocortical fibers. Posterior EEG characteristics in dementia with Lewy bodies (DLB) allowed discrimination of DLB from AD and controls with high accuracy. Traditional EEG systems require a long application time and discomfort, which limited its use in dementia patients. Alternative tools for assessing cognition may be simple, low-cost, and mobile medical devices such as wireless and mobile EEG (wmEEG) sensor platforms with flexible electronics and stretchable electrode sheets that could be compatible with long-term EEG monitoring even in dementia patients. In this study, we review the utility of EEG in reflecting cognitive function and the prospects for clinical application of wmEEG monitoring for detecting early dementia and discriminating subtypes of dementia effectively and objectively assessing longitudinal cognitive changes. Repeated and longitudinal documentation of EEG using wmEEG will contribute to detection of specific sleep/wake EEG patterns for patients with sleep and wake-related problems related to dementia.
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References
1.
HugoJ, GanguliM. Dementia and cognitive impairment: Epidemiology, diagnosis, and treatment. Clin Geriatr Med, 2014; 30:421–442.
2.
ZaritSH. Behavioral disturbances of dementia and caregiver issues. Int Psychogeriatr, 1997; 8(S3):263–268.
3.
SchulzR, O'BrienAT, BookwalaJ, et al.Psychiatric and physical morbidity effects of dementia caregiving: Prevalence, correlates, and causes. Gerontologist, 1995; 35:771–791.
4.
TakechiH, MoriT, HashimotoT, et al.Present status and road map to achieve inclusive and holistic care for dementia in a Japanese community: Analysis using the Delphi method. Dement Geriatr Cogn Disord, 2014; 38:186–199.
5.
FerriCP, PrinceM, BrayneC, et al.Global prevalence of dementia: A Delphi consensus study. Lancet, 2005; 366:2112–2117.
6.
BinettiG, MagniE, PadovaniA, et al.Executive dysfunction in early Alzheimer's disease. J Neurol Neurosurg Psychiatry, 1996; 60:91–93.
7.
ColletteF, Van der LindenM, SalmonE. Executive dysfunction in Alzheimer's disease. Cortex, 1999; 35:57–72.
McKeithIG, BoeveBF, DicksonDW, et al.Diagnosis and management of dementia with Lewy bodies: Fourth consensus report of the DLB Consortium. Neurology, 2017; 89:88–100.
10.
PetersenRC, SmithGE, WaringSC, et al.Mild cognitive impairment: Clinical characterization and outcome. Arch Neurol, 1999; 56:303–308.
11.
PetersenRC, StevensJC, GanguliM, et al.Practice parameter: Early detection of dementia: Mild cognitive impairment (an evidence-based review) Report of the Quality Standards Subcommittee of the American Academy of Neurology. Neurology, 2001; 56:1133–1142.
12.
RitchieK, TouchonJ. Mild cognitive impairment: Conceptual basis and current nosological status. Lancet, 2000; 355:225–228.
13.
AlbertMS, DeKoskyST, DicksonD, et al.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. Alzheimer's Dement, 2011; 7:270–279.
14.
DuboisB, FeldmanHH, JacovaC, et al.Advancing research diagnostic criteria for Alzheimer's disease: The IWG-2 criteria. Lancet Neurol, 2014; 13:614–629.
15.
McCleeryJ, MorganS, BradleyKM, et al.Dopamine transporter imaging for the diagnosis of dementia with Lewy bodies. Cochrane Database Syst Rev, 2015; 1:CD010633.
OlssonB, LautnerR, AndreassonU, et al.CSF and blood biomarkers for the diagnosis of Alzheimer's disease: A systematic review and meta-analysis. Lancet Neurol, 2016; 15:673–684.
18.
JellingerKA, AttemsJ. Prevalence and impact of vascular and Alzheimer pathologies in Lewy body disease. Acta Neuropathol, 2008; 115:427–436.
19.
HowlettDR, WhitfieldD, JohnsonM, et al.Regional multiple pathology scores are associated with cognitive decline in Lewy body dementias. Brain Pathol, 2015; 25:401–408.
20.
KlimeschW, PfurtschellerG, SchimkeH. Pre-and post-stimulus processes in category judgement tasks as measured by event-related desynchronization (ERD). J Psychophysiol, 1992; 6:185–203.
21.
KlimeschW, SchimkeH, SchwaigerJ. Episodic and semantic memory: An analysis in the EEG theta and alpha band. Electroencephalogr Clin Neurophysiol, 1994; 91:428–441.
22.
TorresF, FaoroA, LoewensonR, et al.The electroencephalogram of elderly subjects revisited. Electroencephalogr Clin Neurophysiol, 1983; 56:391–398.
23.
Van Der ZandeJJ, GouwAA, van SteenovenI, et al.EEG characteristics of dementia with Lewy bodies, Alzheimer's disease and mixed pathology. Front Aging Neurosci, 2018; 10:190.
24.
DauwanM, van der ZandeJJ, van DellenE, et al.Random forest to differentiate dementia with Lewy bodies from Alzheimer's disease. Alzheimer's Dement, 2016; 4:99–106.
25.
MichelCM, KoenigT, BrandeisD, et al. Electrical Neuroimaging. Cambridge, United Kingdom, Cambridge University Press, 2009.
26.
JelicV, JohanssonSE, AlmkvistO, et al.Quantitative electroencephalography in mild cognitive impairment: Longitudinal changes and possible prediction of Alzheimer's disease. Neurobiol Aging, 2000; 21:533–540.
27.
PrichepLS, JohnER, FerrisSH, et al.Prediction of longitudinal cognitive decline in normal elderly with subjective complaints using electrophysiological imaging. Neurobiol Aging, 2006; 27:471–481.
28.
PrichepLS. Quantitative EEG and electromagnetic brain imaging in aging and in the evolution of dementia. Ann N Y Acad Sci, 2007; 1097:156–167.
29.
RossiniPM, Del PercioC, PasqualettiP, et al.Conversion from mild cognitive impairment to Alzheimer's disease is predicted by sources and coherence of brain electroencephalography rhythms. Neuroscience, 2006; 143:793–803.
30.
RossiniPM, RossiS, BabiloniC, et al.Clinical neurophysiology of aging brain: From normal aging to neurodegeneration. Prog Neurobiol, 2007; 83:375–400.
31.
LeiserSC, DunlopJ, BowlbyMR, et al.Aligning strategies for using EEG as a surrogate biomarker: A review of preclinical and clinical research. Biochem Pharmacol, 2011; 81:1408–1421.
32.
MorettiDV, PrestiaA, FracassiC, et al.Specific EEG changes associated with atrophy of hippocampus in subjects with mild cognitive impairment and Alzheimer's disease. Int J Alzheimer's Dis, 2012; 2012.
33.
YenerGG, Emek-SavaşDD, LizioR, et al.Frontal delta event-related oscillations relate to frontal volume in mild cognitive impairment and healthy controls. Int J Psychophysiol, 2016; 103:110–117.
34.
DelbeuckX, Van der LindenM, ColletteF. Alzheimer'disease as a disconnection syndrome?. Neuropsychol Rev, 2003; 13:79–92.
35.
VermaNP, GreiffensteinMF, VermaN, et al.Electrophysiologic validation of two categories of dementias—Cortical and subcortical. Clin Electroencephalogr, 1987; 18:26–33.
36.
BergerH. Über das Elektrenkephalogramm des Menschen. Arch Psychiatr Nervenkr, 1931; 94:16–60.
37.
BergerH. über das Elektrenkephalogramm des Menschen. Arch Psychiatr Nervenkr, 1932; 97:6–26.
38.
BrennerRP, UlrichRF, SpikerDG, et al.Computerized EEG spectral analysis in elderly normal, demented and depressed subjects. Electroencephalogr Clin Neurophysiol, 1986; 64:483–492.
39.
CobenLA, DanzigerWL, BergL. Frequency analysis of the resting awake EEG in mild senile dementia of Alzheimer type. Electroencephalogr Clin Neurophysiol, 1983; 55:372–380.
40.
CobenLA, DanzigerW, StorandtM. A longitudinal EEG study of mild senile dementia of Alzheimer type: Changes at 1 year and at 2.5 years. Electroencephalogr Clin Neurophysiol, 1985; 61:101–112.
41.
GiaquintoS, NolfeG. The EEG in the normal elderly: A contribution to the interpretation of aging and dementia. Electroencephalogr Clin Neurophysiol, 1986; 63:540–546.
42.
DunkinJJ, LeuchterAF, NewtonTF, et al.Reduced EEG coherence in dementia: State or trait marker?. Biol Psychiatry, 1994; 35:870–879.
43.
LeuchterAF, SparJE, WalterDO, et al.Electroencephalographic spectra and coherence in the diagnosis of Alzheimer's-type and multi-infarct dementia: A pilot study. Arch Gen Psychiatry, 1987; 44:993–998.
44.
LocatelliT, CursiM, LiberatiD, et al.EEG coherence in Alzheimer's disease. Electroencephalogr Clin Neurophysiol, 1998; 106:229–237.
45.
LeuchterAF, NewtonTF, CookIA, et al.Changes in brain functional connectivity in Alzheimer-type and multi-infarct dementia. Brain, 1992; 115:1543–1561.
46.
BesthornC, FörstlH, Geiger-KabischC, et al.EEG coherence in Alzheimer disease. Clin Neurophysiol, 1994; 90:242–245.
47.
StomrudE, HanssonO, MinthonL, et al.Slowing of EEG correlates with CSF biomarkers and reduced cognitive speed in elderly with normal cognition over 4 years. Neurobiol Aging, 2010; 31:215–223.
48.
HughesJR, ShanmughamS, WetzelLC, et al.The relationship between EEG changes and cognitive functions in dementia: A study in a VA population. Clin Electroencephalogr, 1989; 20:77–85.
49.
KowalskiJW, GawelM, PfefferA, et al.The diagnostic value of EEG in Alzheimer disease: Correlation with the severity of mental impairment. J Clin Neurophysiol, 2001; 18:570–575.
50.
BastiaansenMCM, PosthumaD, GrootPFC, et al.Event-related alpha and theta responses in a visuo-spatial working memory task. Clin Neurophysiol, 2002; 113:1882–1893.
51.
TescheCD, KarhuJ. Theta oscillations index human hippocampal activation during a working memory task. Proc Natl Acad Sci, 2000; 97:919–924.
52.
KlimeschW. Memory processes, brain oscillations and EEG synchronization. Int J Psychophysiol, 1996; 24:61–100.
53.
CavanaghJF, FrankMJ. Frontal theta as a mechanism for cognitive control. Trends Cogn Sci, 2014; 18:414–421.
54.
FermanTJ, SmithGE, BoeveBF, et al.DLB fluctuations: Specific features that reliably differentiate DLB from AD and normal aging. Neurology, 2004; 62:181–187.
55.
BonanniL, ThomasA, TiraboschiP, et al.EEG comparisons in early Alzheimer's disease, dementia with Lewy bodies and Parkinson's disease with dementia patients with a 2-year follow-up. Brain, 2008; 131:690–705.
56.
LeeH, BrekelmansGJF, RoksG. The EEG as a diagnostic tool in distinguishing between dementia with Lewy bodies and Alzheimer's disease. Clin Neurophysiol, 2015; 126:1735–1739.
57.
RoksG, KorfESC, van der FlierWM, et al.The use of EEG in the diagnosis of dementia with Lewy bodies. J Neurol Neurosurg Psychiatry, 2008; 79:377–380.
58.
DauwelsJ, VialatteF, CichockiA. Diagnosis of Alzheimer's disease from EEG signals: Where are we standing?. Curr Alzheimer Res, 2010; 7:487–505.
59.
BucknerRL, SepulcreJ, TalukdarT, et al.Cortical hubs revealed by intrinsic functional connectivity: Mapping, assessment of stability, and relation to Alzheimer's disease. J Neurosci, 2009; 29:1860–1873.
60.
de HaanW, PijnenburgYAL, StrijersRLM, et al.Functional neural network analysis in frontotemporal dementia and Alzheimer's disease using EEG and graph theory. BMC Neurosci, 2009; 10:101.
61.
SperlingRA, AisenPS, BeckettLA, et al.Toward defining the preclinical stages of Alzheimer's disease: Recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease. Alzheimers Dement,, 2011; 7:280–292.
62.
de HaanW, van der FlierWM, KoeneT, et al.Disrupted modular brain dynamics reflect cognitive dysfunction in Alzheimer's disease. Neuroimage, 2012; 59:3085–3093.
63.
HeY, ChenZ, EvansA. Structural insights into aberrant topological patterns of large-scale cortical networks in Alzheimer's disease. J Neurosci, 2008; 28:4756–4766.
64.
RodriguezE, GeorgeN, LachauxJ-P, et al.Perception's shadow: Long-distance synchronization of human brain activity. Nature, 1999; 397:430.
65.
SingerW. Consciousness and the binding problem. Ann N Y Acad Sci, 2001; 929:123–146.
66.
SummerfieldC, JackAI, BurgessAP. Induced gamma activity is associated with conscious awareness of pattern masked nouns. Int J Psychophysiol, 2002; 44:93–100.
67.
VarelaF, LachauxJP, RodriguezE, et al.The brainweb: Phase synchronization and large-scale integration. Nat Rev Neurosci, 2001; 2:229–239.
68.
LehmannC, KoenigT, JelicV, et al.Application and comparison of classification algorithms for recognition of Alzheimer's disease in electrical brain activity (EEG). J Neurosci Methods, 2007; 161:342–350.
69.
GhomashchiA, ZhengZ, MajajN, et al. A low-cost, open-source, wireless electrophysiology system. In: 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2014: 3138–3141.
70.
LovelaceJA, WittTS, BeyetteFR. Modular, bluetooth enabled, wireless electroencephalograph (EEG) platform. In: 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2013: 6361–6364.
71.
NathanV, JafariR. Characterizing contact impedance, signal quality and robustness as a function of the cardinality and arrangement of fingers on dry contact EEG electrodes. In: 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2014: 3755–3758.
72.
SekitaniT, YoshimotoS, ArakiT, et al. 12-2: Invited Paper: A Sheet-type Wireless electroencephalogram (EEG) Sensor System using Flexible and Stretchable Electronics. In: SID Symposium Digest of Technical Papers. Vol. 48. Wiley Online Library, 2017: 143–146.
73.
GarnierF, HajlaouiR, YassarA, et al.All-polymer field-effect transistor realized by printing techniques. Science, 1994; 265:1684–1686.
74.
ForrestSR. The path to ubiquitous and low-cost organic electronic appliances on plastic. Nature, 2004; 428:911.
75.
KimD-H, AhnJ-H, ChoiWM, et al.Stretchable and foldable silicon integrated circuits. Science, 2008; 320:507–511.
76.
MarienH, SteyaertMSJ, van VeenendaalE, et al.Analog building blocks for organic smart sensor systems in organic thin-film transistor technology on flexible plastic foil. IEEE J Solid State Circuits, 2012; 47:1712–1720.
77.
FuketaH, YoshiokaK, ShinozukaY, et al. 1μm-thickness 64-channel surface electromyogram measurement sheet with 2V organic transistors for prosthetic hand control. In: 2013 IEEE International Solid-State Circuits Conference Digest of Technical Papers (ISSCC). IEEE, 2013: 104–105.
78.
YoshimotoS, ArakiT, UemuraT, et al. Wireless EEG patch sensor on forehead using on-demand stretchable electrode sheet and electrode-tissue impedance scanner. In: 2016 IEEE 38th Annual International Conference of The Engineering in Medicine and Biology Society (EMBC). IEEE, 2016: 6286–6289.
79.
BoeveBF. REM sleep behavior disorder: Updated review of the core features, the REM sleep behavior disorder-neurodegenerative disease association, evolving concepts, controversies, and future directions. Ann N Y Acad Sci, 2010; 1184:15–54.
80.
IranzoA, IsettaV, MolinuevoJL, et al.Electroencephalographic slowing heralds mild cognitive impairment in idiopathic REM sleep behavior disorder. Sleep Med, 2010; 11:534–539.
81.
Rodrigues BrazeteJ, MontplaisirJ, PetitD, et al.Electroencephalogram slowing in rapid eye movement sleep behavior disorder is associated with mild cognitive impairment. Sleep Med, 2013; 14:1059–1063.
82.
BangYR, JeonHJ, YounS, et al.Alterations of awake EEG in idiopathic REM sleep behavior disorder without cognitive impairment. Neurosci Lett, 2017; 637:64–69.
83.
MusiekES, BhimasaniM, ZangrilliMA, et al.Circadian rest-activity pattern changes in aging and preclinical Alzheimer Disease. JAMA Neurol, 2018; 75:582–590.
84.
JuY-ES, McLelandJS, ToedebuschCD, et al.Sleep quality and preclinical Alzheimer disease. JAMA Neurol, 2013; 70:587–593.
