Background: Presenilin-1 (PSEN1) mutations are the most common cause of familial early onset Alzheimer’s disease (AD). The PSEN1 E280A (E280A) mutation has an autosomal dominant inheritance and is involved in the production of amyloid-β. The largest family group of carriers with E280A mutation is found in Antioquia, Colombia. The study of mutation carriers provides a unique opportunity to identify brain changes in stages previous to AD. Electroencephalography (EEG) is a low cost and minimally invasiveness technique that enables the following of brain changes in AD.
Objective: To examine how previous reported differences in EEG for Theta and Alpha-2 rhythms in E280A subjects are related to specific regions in cortex and could be tracked across different ages.
Methods: EEG signals were acquired during resting state from non-carriers and carriers, asymptomatic and symptomatic subjects from E280A kindred from Antioquia, Colombia. Independent component analysis (ICA) and inverse solution methods were used to locate brain regions related to differences in Theta and Alpha-2 bands.
Results: ICA identified two components, mainly related to the Precuneus, where the differences in Theta and Alpha-2 exist simultaneously at asymptomatic and symptomatic stages. When the ratio between Theta and Alpha-2 is used, significant correlations exist with age and a composite cognitive scale.
Conclusion: Theta and Alpha-2 rhythms are altered in E280A subjects. The alterations are possible to track at Precuneus regions using EEG, ICA, and inverse solution methods.
LatypovaX, MartinL (2014) 2015: Which new directions for Alzheimer’s disease?Front Cell Neurosci8, 417.
2.
AlvesL, CorreiaASA, MiguelR, AlegriaP, BugalhoP (2012) Alzheimer’s disease: A clinical practice-oriented review. Front Neurol3, 63.
3.
LoperaF, ArdillaA, MartínezA, MadrigalL, Arango-VianaJC, LemereCA, Arango-LasprillaJC, HincapiéL, Arcos-BurgosM, OssaJE, BehrensIM, NortonJ, LendonC, GoateAM, Ruiz-LinaresA, RosselliM, KosikKS (1997) Clinical features of early-onset Alzheimer disease in a large kindred with an E280A presenilin-1 mutation. J Am Med Assoc277, 793–799.
Arango LasprillaJC, IglesiasJ, LoperaF (2003) Neuropsychological study of familial Alzheimer’s disease caused by mutation E280A in the presenilin 1 gene. Am J Alzheimers Dis Other Demen18, 137–146.
6.
LangbaumJB, FleisherAS, ChenK, AyutyanontN, LoperaF, QuirozYT, CaselliRJ, TariotPN, ReimanEM (2013) Ushering in the study and treatment of preclinical Alzheimer disease. Nat Rev Neurol9, 371–381.
7.
SperlingR, MorminoE, JohnsonK (2014) The evolution of preclinical Alzheimer’s disease: Implications for prevention trials. Neuron84, 608–622.
8.
WurtmanR (2015) Biomarkers in the diagnosis and management of Alzheimer’s disease. Metabolism64, S47–S50.
FleisherAS, ChenK, QuirozYT, JakimovichLJ, GomezMG, LangoisCM, LangbaumJB, AyutyanontN, RoontivaA, ThiyyaguraP, LeeW, MoH, LopezL, MorenoS, Acosta-BaenaN, GiraldoM, GarciaG, ReimanRA, HuentelmanMJ, KosikKS, TariotPN, LoperaF, ReimanEM (2012) Florbetapir PET analysis of amyloid-β deposition in the presenilin 1 E280A autosomal dominant Alzheimer’sdisease kindred: A cross-sectional study. Lancet Neurol11, 1057–1065.
11.
QuirozYT, BudsonAE, CeloneK, RuizA, NewmarkR, CastrillónG, LoperaF, SternCE (2010) Hippocampal hyperactivation in presymptomatic familial Alzheimer’s disease. Ann Neurol68, 865–875.
12.
QuirozYT, WillmentKC, CastrillonG, MunizM, LoperaF, BudsonA, SternCE (2015) Successful scene encoding in presymptomatic early-onset Alzheimer’s disease. J Alzheimers Dis47, 955–964.
13.
LondonoAC, CastellanosFX, ArbelaezA, RuizA, Aguirre-AcevedoDC, RichardsonAM, EastealS, LidburyBA, Arcos-BurgosM, LoperaF (2014) An 1H-MRS framework predicts the onset of Alzheimer’s disease symptoms in PSEN1 mutation carriers. Alzheimers Dement10, 552–561.
14.
QuirozYT, SchultzAP, ChenK, ProtasHD, BrickhouseM, FleisherAS, LangbaumJB, ThiyyaguraP, FaganAM, ShahAR, MunizM, Arboleda-VelasquezJF, MunozC, GarciaG, Acosta-BaenaN, GiraldoM, TiradoV, RamírezDL, TariotPN, DickersonBC, SperlingRA, LoperaF, ReimanEM (2015) Brain Imaging and blood biomarker abnormalities in children with autosomal dominant Alzheimer disease. JAMA Neurol72, 912.
15.
MicanovicC, PalS (2014) The diagnostic utility of EEG in early-onset dementia: A systematic review of the literature with narrative analysis. J Neural Transm121, 59–69.
16.
BobesMA, GarcíaYF, LoperaF, QuirozYT, GalánL, VegaM, TrujilloN, Valdes-SosaM, Valdes-SosaP (2010) ERP generator anomalies in presymptomatic carriers of the Alzheimer’s disease E280A PS-1 mutation. Hum Brain Mapp31, 247–265.
OchoaJ, SánchezF, TobónC, DuqueJ, QuirozY, LoperaF, HernandezM. (2015) Effective connectivity changes in presymptomatic Alzheimer’s disease with E280A presenilin-1 mutation gene. In VI Latin American Congress on Biomedical Engineering CLAIB 2014, Paraná Argentina 29, 30 & 31 October 2014 SE - 130, BraidotA, HadadA, eds. Springer International Publishing, pp. 508–511.
PiettoM, ParraMA, TrujilloN, FloresF, GarcíaAM, BustinJ, RichlyP, ManesF, LoperaF, IbáñezA, BaezS (2016) Behavioral and electrophysiological correlates of memory binding deficits in patients at different risk levels for Alzheimer’s disease. J Alzheimers Dis53, 1325–1340.
21.
RodriguezR, LoperaF, AlvarezA, FernandezY, GalanL, QuirozY, BobesMA (2014) Spectral analysis of EEG in familial Alzheimer’s disease with E280A presenilin-1 mutation gene. Int J Alzheimers Dis2014, 180741.
22.
DuqueJE, TobonC, AponteC, OchoaJF, MuñozC, HernándezAM, QuirozY, LoperaF (2014) Quantitative EEG analysis disease during resting and memory task in carriers and non-carriers of PS-1 E280A mutation of familial Alzheimer’s. CES Med28, 165–176.
23.
BabiloniC, LizioR, MarzanoN, CapotostoP, SoricelliA, TriggianiAI, CordoneS, GesualdoL, Del PercioC (2016) Brain neural synchronization and functional coupling in Alzheimer’s disease as revealed by resting state EEG rhythms. Int J Psychophysiol103, 88–102.
24.
HammV, HéraudC, CasselJ-C, MathisC, GoutagnyR (2015) Precocious alterations of brain oscillatory activity in Alzheimer’s disease: A window of opportunity for early diagnosis and treatment. Front Cell Neurosci9, 491.
ChenJ-L, RosT, GruzelierJH (2013) Dynamic changes of ICA-derived EEG functional connectivity in the resting state. Hum Brain Mapp34, 852–868.
33.
OchoaJF, RuizM, ValleD, DuqueJ, TobonC, AlonsoJF, HernandezAM, MananasMA (2015) Neurophysiological correlates in mild cognitive impairment detected using group independent component analysis. Conf Proc IEEE Eng Med Biol Soc2015, 7442–7445.
34.
ZervakisM, MichalopoulosK, IordanidouV, SakkalisV (2011) Intertrial coherence and causal interaction among independent EEG components. J Neurosci Methods197, 302–314.
35.
PonomarevVA, MuellerA, CandrianG, Grin-YatsenkoVA, KropotovJD (2014) Group independent componentanalysis (gICA) and current source density (CSD) in the study of EEG in ADHD adults. Clin Neurophysiol125, 83–97.
36.
JatoiMA, KamelN, MalikAS, FayeI, BegumT (2014) A survey of methods used for source localization using EEG signals. Biomed Signal Process Control11, 42–52.
37.
AyutyanontN, LangbaumJBS, HendrixSB, ChenK, FleisherAS, FriesenhahnM, WardM, AguirreC, Acosta-BaenaN, MadrigalL, MuñozC, TiradoV, MorenoS, TariotPN, LoperaF, ReimanEM (2014) The Alzheimer’s prevention initiative composite cognitive test score: Sample size estimates for the evaluation of preclinical Alzheimer’sdisease treatments in presenilin 1 E280A mutation carriers. J Clin Psychiatry75, 652–660.
38.
ShirerWR, RyaliS, RykhlevskaiaE, MenonV, GreiciusMD (2012) Decoding subject-driven cognitive states with whole-brain connectivity patterns. Cereb Cortex22, 158–165.
39.
Aguirre-AcevedoDC, GómezRD, MorenoS, Henao-ArboledaE, MottaM, MuñozC, AranaA, PinedaDA, LoperaF. [Validity and reliability of the CERAD-Col neuropsychological battery]. Rev Neurol45, 655–660.
40.
SuarezX, OchoaJ, DuqueJ (2016) Improving test-retest reliability of quantitative electroencephalography using different preprocessing approaches. Conf Proc IEEE Eng Med Biol Soc2016, 961–964.
41.
Bigdely-ShamloN, MullenT, KotheC, SuK-M, RobbinsKA (2015) The PREP pipeline: Standardized preprocessing for large-scale EEG analysis. Front Neuroinform9, 16.
42.
DelormeA, MakeigS (2004) EEGLAB: An open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J Neurosci Methods134, 9–21.
LiY-O, AdaliT, CalhounVD (2007) Estimating the number of independent components for functional magnetic resonance imaging data. Hum Brain Mapp28, 1251–1266.
45.
HyvärinenA, RamkumarP, ParkkonenL, HariR (2010) Independent component analysis of short-time Fourier transforms for spontaneous EEG/MEG analysis. Neuroimage49, 257–271.
46.
HimbergJ, HyvärinenA, EspositoF (2004) Validating the independent components of neuroimaging time series via clustering and visualization. Neuroimage22, 1214–1222.
47.
Pascual-MarquiRD, LehmannD, KoukkouM, KochiK, AndererP, SaletuB, TanakaH, HirataK, JohnER, PrichepL, Biscay-LirioR, KinoshitaT (2011) Assessing interactions in the brain with exact low-resolution electromagnetic tomography. Philos Trans A Math Phys Eng Sci369, 3768–3784.
48.
Pascual-MarquiRD (2007) Discrete, 3D distributed, linear imaging methods of electric neuronal activity. Part 1: Exact, zero error localization. arXiv:0710.3341v2
49.
HeB, DaiY, AstolfiL, BabiloniF, YuanH, YangL (2011) eConnectome: A MATLAB toolbox for mapping and imaging of brain functional connectivity. J Neurosci Methods195, 261–269.
50.
MitraP, BokilH (2007), Observed Brain Dynamics, Oxford University Press.
BabiloniC, BinettiG, CassarinoA, Dal FornoG, Del PercioC, FerreriF, FerriR, FrisoniG, GalderisiS, HirataK, LanuzzaB, MiniussiC, MucciA, NobiliF, RodriguezG, Luca RomaniG, RossiniPM (2006) Sources of cortical rhythms in adults during physiological aging: A multicentric EEG study. Hum Brain Mapp27, 162–172.
56.
PrichepLS, JohnER, FerrisSH, RauschL, FangZ, CancroR, TorossianC, ReisbergB (2006) Prediction of longitudinal cognitive decline in normal elderly with subjective complaints using electrophysiological imaging. Neurobiol Aging27, 471–481.
57.
BhattacharyaBS, CoyleD, MaguireLP (2011) Alpha and theta rhythm abnormality in Alzheimer’s Disease: A study using a computational model. Adv Exp Med Biol718, 57–73.
BabiloniC, BinettiG, CassettaE, FornoGD, Del PercioC, FerreriF, FerriR, FrisoniG, HirataK, LanuzzaB, MiniussiC, MorettiDV, NobiliF, RodriguezG, RomaniGL, SalinariS, RossiniPM (2006) Sources of cortical rhythms change as a function of cognitive impairment in pathological aging: A multicenter study. Clin Neurophysiol117, 252–268.
60.
BabiloniC, VisserPJ, FrisoniG, De DeynPP, BrescianiL, JelicV, NagelsG, RodriguezG, RossiniPM, VecchioF, ColomboD, VerheyF, WahlundL-O, NobiliF (2010) Cortical sources of resting EEG rhythms in mild cognitive impairment and subjective memory complaint. Neurobiol Aging31, 1787–1798.
61.
BabiloniC, CarducciF, LizioR, VecchioF, BaglieriA, BernardiniS, CavedoE, BozzaoA, ButtinelliC, EspositoF, GiubileiF, GuizzaroA, MarinoS, MontellaP, QuattrocchiCC, RedolfiA, SoricelliA, TedeschiG, FerriR, Rossi-FedeleG, UrsiniF, ScrasciaF, VernieriF, PedersenTJ, HardemarkH-G, RossiniPM, FrisoniGB (2013) Resting state cortical electroencephalographic rhythms are related to gray matter volume in subjects with mildcognitive impairment and Alzheimer’s disease. Hum Brain Mapp34, 1427–1446.
62.
BabiloniC, FrisoniGB, PievaniM, VecchioF, LizioR, ButtiglioneM, GeroldiC, FracassiC, EusebiF, FerriR, RossiniPM (2009) Hippocampal volume and cortical sources of EEG alpha rhythms in mild cognitive impairment and Alzheimer disease. Neuroimage44, 123–135.
63.
BabiloniC, LizioR, CarducciF, VecchioF, RedolfiA, MarinoS, TedeschiG, MontellaP, GuizzaroA, EspositoF, BozzaoA, GiubileiF, OrziF, QuattrocchiCC, SoricelliA, SalvatoreE, BaglieriA, BramantiP, CavedoE, FerriR, CosentinoF, FerraraM, MundiC, GrilliG, PuglieseS, GerardiG, ParisiL, VernieriF, TriggianiAI, PedersenJT, HårdemarkH-G, RossiniPM, FrisoniGB (2011) Resting state cortical electroencephalographic rhythms and white matter vascular lesions in subjects with Alzheimer’sdisease: An Italian multicenter study. J Alzheimers Dis26, 331–346.
PonomarevaN, AndreevaT, ProtasovaM, ShagamL, MalinaD, GoltsovA, FokinV, MitrofanovA, RogaevE (2013) Age-dependent effect of Alzheimer’s risk variant of CLU on EEG alpha rhythm in non-demented adults. Front Aging Neurosci5, 86.
66.
HatzF, BenzN, HardmeierM, ZimmermannR, RueeggS, SchindlerC, MiserezAR, GschwandtnerU, MonschAU, FuhrP (2013) Quantitative EEG and apolipoprotein E-genotype improve classification of patients with suspected Alzheimer’s disease. Clin Neurophysiol124, 2146–2152.
67.
CanuetL, TelladoI, CouceiroV, FraileC, Fernandez-NovoaL, IshiiR, TakedaM, CacabelosR (2012) Resting-state network disruption and APOE genotype in Alzheimer’sdisease: A lagged functional connectivity study. PLoS One7, e46289.
68.
KramerG, van der FlierWM, de LangenC, BlankensteinMA, ScheltensP, StamCJ (2008) EEG functional connectivity and ApoE genotype in Alzheimer’s disease and controls. Clin Neurophysiol119, 2727–2732.
69.
CuestaP, BarabashA, AurtenetxeS, GarcésP, LópezME, BajoR, Llanero-LuqueM, AncínI, CabranesJA, MarcosA, SanchoM, NakamuraA, MaestúF, FernandezA (2015) Source analysis of spontaneous magnetoencephalograpic activity in healthy aging and mild cognitive impairment: Influence of apolipoprotein E polymorphism. J Alzheimers Dis43, 259–273.
70.
CuestaP, GarcésP, CastellanosNP, LópezME, AurtenetxeS, BajoR, Pineda-PardoJA, BruñaR, MarínAG, DelgadoM, BarabashA, AncínI, CabranesJA, FernandezA, Del PozoF, SanchoM, MarcosA, NakamuraA, MaestúF (2015) Influence of the APOE ɛ4 allele and mild cognitive impairment diagnosis in the disruption of the MEG resting state functional connectivity in sources space. J Alzheimers Dis44, 493–505.
71.
de WaalH, StamCJ, de HaanW, van StraatenECW, BlankensteinMA, ScheltensP, van der FlierWM (2013) Alzheimer’s disease patients not carrying the apolipoprotein E ɛ4 allele show more severe slowing of oscillatory brain activity. Neurobiol Aging34, 2158–2163.
72.
PonomarevaNV, KorovaitsevaGI, RogaevEI (2008) EEG alterations in non-demented individuals related to apolipoprotein E genotype and to risk of Alzheimer disease. Neurobiol Aging29, 819–827.
73.
van StraatenEC, ScheltensP, GouwAA, StamCJ (2014) Eyes-closed task-free electroencephalography in clinical trials for Alzheimer’s disease: An emerging method based upon brain dynamics. Alzheimers Res Ther6, 86.
74.
BabiloniC, TriggianiAI, LizioR, CordoneS, TattoliG, BevilacquaV, SoricelliA, FerriR, NobiliF, GesualdoL, Millán-CalentiJC, BujánA, TortelliR, CardinaliV, BarulliMR, GianniniA, SpagnoloP, ArmeniseS, BuenzaG, ScianaticoG, LogroscinoG, FrisoniGB, del PercioC (2016) Classification of single normal and Alzheimer’sdisease individuals from cortical sources of resting state EEG rhythms. Front Neurosci10, 47.
75.
SchmidtMT, KandaPAM, BasileLFH, da Silva LopesHF, BarathoR, DemarioJLC, JorgeMS, NardiAE, MachadoS, IanofJN, NitriniR, AnghinahR (2013) Index of alpha/theta ratio of the electroencephalogram: A new marker for Alzheimer’s disease. Front Aging Neurosci5, 60.
JungTP, MakeigS, WesterfieldM, TownsendJ, CourchesneE, SejnowskiTJ (2000) Removal of eye activity artifacts from visual event-related potentials in normal and clinical subjects. Clin Neurophysiol111, 1745–1758.
RisslingAJ, MiyakoshiM, SugarCA, BraffDL, MakeigS, LightGA (2014) Cortical substrates and functional correlates of auditory deviance processing deficits in schizophrenia. NeuroImage Clin6, 424–437.
80.
CongedoM, JohnRE, De RidderD, PrichepL (2010) Group independent component analysis of resting state EEG in large normative samples. Int J Psychophysiol78, 89–99.
81.
HusterRJ, PlisSM, CalhounVD (2015) Group-level component analyses of EEG: Validation and evaluation. Front Neurosci9, 254.
82.
LioG, BoulinguezP (2016) How does sensor-space group blind source separation face inter-individual neuroanatomical variability? Insights from a simulation study based on the PALS-B12 atlas. Brain Topogr, doi: 10.1007/s10548-016-0497-z
83.
Van EssenDC (2005) A population-average, landmark- and surface-based (PALS) atlas of human cerebral cortex. Neuroimage28, 635–662.
84.
MišićB, BetzelRF, de ReusMA, van den HeuvelMP, BermanMG, McIntoshAR, SpornsO (2016) Network-level structure-function relationships in human neocortex. Cereb Cortex26, 3285–3296.
85.
van den HeuvelMP, SpornsO (2013) Network hubs in the human brain. Trends Cogn Sci17, 683–696.
BucknerRL, SepulcreJ, TalukdarT, KrienenFM, LiuH, HeddenT, Andrews-HannaJR, SperlingRA, JohnsonKA (2009) Cortical hubs revealed by intrinsic functional connectivity: Mapping, assessment of stability, and relation to Alzheimer’s disease. J Neurosci29, 1860–1873.
88.
OhH, MadisonC, BakerS, RabinoviciG, JagustW (2016) Dynamic relationships between age, amyloid-β deposition, and glucose metabolism link to the regional vulnerability to Alzheimer’s disease. Brain139, 2275–2289.
89.
ReimanEM, QuirozYT, FleisherAS, ChenK, Velez-PardoC, Jimenez-Del-RioM, FaganAM, ShahAR, AlvarezS, ArbelaezA, GiraldoM, Acosta-BaenaN, SperlingRA, DickersonB, SternCE, TiradoV, MunozC, ReimanRA, HuentelmanMJ, AlexanderGE, LangbaumJBS, KosikKS, TariotPN, LoperaF (2012) Brain imaging and fluid biomarker analysis in young adults at genetic risk for autosomal dominant Alzheimer’s disease in the presenilin 1 E280A kindred: A case-control study. Lancet Neurol11, 1048–1056.
90.
FleisherAS, ChenK, QuirozYT, JakimovichLJ, Gutierrez GomezM, LangoisCM, LangbaumJBS, RoontivaA, ThiyyaguraP, LeeW, AyutyanontN, LopezL, MorenoS, MuñozC, TiradoV, Acosta-BaenaN, FaganAM, GiraldoM, GarciaG, HuentelmanMJ, TariotPN, LoperaF, ReimanEM (2015) Associations between biomarkers and age in the presenilin 1 E280A autosomal dominant Alzheimer disease kindred: A cross-sectional study. JAMA Neurol72, 316–324.
