Integrative EEG biomarkers predict progression to Alzheimer's disease at the MCI stage

被引:133
|
作者
Poil, Simon-Shlomo [1 ]
de Haan, Willem [2 ,3 ,4 ]
van der Flier, Wiesje M. [4 ,5 ]
Mansvelder, Huibert D. [1 ]
Scheltens, Philip [4 ]
Linkenkaer-Hansen, Klaus [1 ]
机构
[1] Vrije Univ Amsterdam, Ctr Neurogen & Cognit Res, Dept Integrat Neurophysiol, NL-1081 HV Amsterdam, Netherlands
[2] Vrije Univ Amsterdam, Dept Clin Neurophysiol, NL-1081 HV Amsterdam, Netherlands
[3] Vrije Univ Amsterdam, MEG, NL-1081 HV Amsterdam, Netherlands
[4] Vrije Univ Amsterdam, Alzheimer Ctr, Dept Neurol, NL-1081 HV Amsterdam, Netherlands
[5] Vrije Univ Amsterdam, Dept Epidemiol & Biostat, NL-1081 HV Amsterdam, Netherlands
来源
关键词
Neurophysiological Biomarkers; Alzheimer's disease; mild cognitive impairment (MCI); electroencephalography; predictive analysis; time series analysis; eyes closed resting state; MILD COGNITIVE IMPAIRMENT; DETRENDED FLUCTUATION ANALYSIS; THETA-OSCILLATIONS; QUANTITATIVE EEG; ASSOCIATION WORKGROUPS; DIAGNOSTIC GUIDELINES; TEMPORAL CORRELATIONS; NATIONAL INSTITUTE; VASCULAR DAMAGE; WORKING-MEMORY;
D O I
10.3389/fnagi.2013.00058
中图分类号
R592 [老年病学]; C [社会科学总论];
学科分类号
03 ; 0303 ; 100203 ;
摘要
Alzheimer's disease (AD) is a devastating disorder of increasing prevalence in modern society. Mild cognitive impairment (MCI) is considered a transitional stage between normal aging and AD; however, not all subjects with MCI progress to AD. Prediction of conversion to AD at an early stage would enable an earlier, and potentially more effective, treatment of AD. Electroencephalography (EEG) biomarkers would provide a non-invasive and relatively cheap screening tool to predict conversion to AD; however, traditional EEG biomarkers have not been considered accurate enough to be useful in clinical practice. Here, we aim to combine the information from multiple EEG biomarkers into a diagnostic classification index in order to improve the accuracy of predicting conversion from MCI to AD within a 2-year period. We followed 86 patients initially diagnosed with MCI for 2 years during which 25 patients converted to AD. We show that multiple EEG biomarkers mainly related to activity in the beta-frequency range (13-30 Hz) can predict conversion from MCI to AD. Importantly, by integrating six EEG biomarkers into a diagnostic index using logistic regression the prediction improved compared with the classification using the individual biomarkers, with a sensitivity of 88% and specificity of 82%, compared with a sensitivity of 64% and specificity of 62% of the best individual biomarker in this index. In order to identify this diagnostic index we developed a data mining approach implemented in the Neurophysiological Biomarker Toolbox (http://www.nbtwiki.net/). We suggest that this approach can be used to identify optimal combinations of biomarkers (integrative biomarkers) also in other modalities. Potentially, these integrative biomarkers could be more sensitive to disease progression and response to therapeutic intervention.
引用
收藏
页数:12
相关论文
共 50 条
  • [41] Depression and Alzheimer's Disease Biomarkers Predict Driving Decline
    Babulal, Ganesh M.
    Chen, Suzie
    Williams, Monique M.
    Trani, Jean-Francois
    Bakhshi, Parul
    Chao, Grace L.
    Stout, Sarah H.
    Fagan, Anne M.
    Benzinger, Tammie L. S.
    Holtzman, David M.
    Morris, John C.
    Roe, Catherine M.
    JOURNAL OF ALZHEIMERS DISEASE, 2018, 66 (03) : 1213 - 1221
  • [42] Blood plasma biomarkers could predict Alzheimer's disease
    Nature Clinical Practice Neurology, 2008, 4 (1): : 7 - 7
  • [43] Biomarkers may help predict risk of Alzheimer's disease
    不详
    BIOMARKERS IN MEDICINE, 2009, 3 (05) : 563 - 563
  • [44] IMAGING AND COGNITIVE BIOMARKERS AS PREDICTORS OF PROGRESSION TO ALZHEIMER'S DISEASE
    Rowe, C. C.
    Ellis, K.
    Brown, B.
    Bourgeat, P.
    Jones, G.
    Salvado, O.
    Martins, R.
    Masters, C. L.
    Ames, D.
    Villemagne, V. L.
    INTERNAL MEDICINE JOURNAL, 2012, 42 : 13 - 13
  • [45] Biomarkers of Alzheimer's disease pathology progression in Down syndrome
    De Deyn, P. P.
    Dekker, A. D.
    EUROPEAN NEUROPSYCHOPHARMACOLOGY, 2016, 26 : S114 - S114
  • [46] Imaging and cognitive biomarkers as predictors of progression to Alzheimer's disease
    Rowe, Christopher
    Ellis, Kathryn
    Brown, Belinda
    Bourgeat, Pierrick
    Faux, Noel
    Martins, Ralph
    Salvado, Olivier
    Masters, Colin
    Ames, David
    Villemagne, Victor
    JOURNAL OF NUCLEAR MEDICINE, 2012, 53
  • [47] Insulin Resistance and CSF Biomarkers in Puerto Ricans with MCI and Early Alzheimer's Disease
    Sepulveda, V.
    Arnold, S.
    Jimenez, I.
    Wojna, V.
    JOURNAL OF THE AMERICAN GERIATRICS SOCIETY, 2014, 62 : S232 - S232
  • [48] Plasma isoprostanoids assessment as Alzheimer's disease progression biomarkers
    Pena-Bautista, Carmen
    alvarez, Lourdes
    Baquero, Miguel
    Ferrer, Ines
    Garcia, Lorena
    Hervas-Marin, David
    Chafer-Pericas, Consuelo
    JOURNAL OF NEUROCHEMISTRY, 2021, 157 (06) : 2187 - 2194
  • [49] Whole brain atrophy rate predicts progression from MCI to Alzheimer's disease
    Spulber, Gabriela
    Niskanen, Eini
    MacDonald, Stuart
    Smilovici, Oded
    Chen, Kewei
    Reiman, Eric M.
    Jauhiainen, Anne M.
    Hallikainen, Merja
    Tervo, Susanna
    Wahlund, Lars-Olof
    Vanninen, Ritva
    Kivipelto, Miia
    Soininen, Hilkka
    NEUROBIOLOGY OF AGING, 2010, 31 (09) : 1601 - 1605
  • [50] Automated MRI measures predict progression to Alzheimer's disease
    Desikan, Rahul S.
    Cabral, Howard J.
    Settecase, Fabio
    Hess, Christopher P.
    Dillon, William P.
    Glastonbury, Christine M.
    Weiner, Michael W.
    Schmansky, Nicholas J.
    Salat, David H.
    Fischl, Bruce
    NEUROBIOLOGY OF AGING, 2010, 31 (08) : 1364 - 1374