Decrease of non-linear structure in the EEG of Alzheimer patients compared to healthy controls

被引:114
|
作者
Jelles, B
van Birgelen, JH
Slaets, JPJ
Hekster, REM
Jonkman, EJ
Stam, CJ
机构
[1] Leyenburg Hosp, Dept Neurol & Clin Neurophysiol, The Hague, Netherlands
[2] Leyenburg Hosp, Dept Clin Psychol, The Hague, Netherlands
[3] Leyenburg Hosp, Dept Clin Geriatr, The Hague, Netherlands
[4] Leyenburg Hosp, Dept Radiol, The Hague, Netherlands
[5] Hosp Vrije Univ, Dept Clin Neurophysiol, Amsterdam, Netherlands
关键词
Alzheimer's disease; electroencephalography; non-linear dynamics; chaos; correlation dimension;
D O I
10.1016/S1388-2457(99)00013-9
中图分类号
R74 [神经病学与精神病学];
学科分类号
摘要
Objective: Non-linear EEG analysis can provide information about the functioning of neural networks that cannot be obtained with linear analysis. The correlation dimension (D-2) is considered to be a reflection of the complexity of the cortical dynamics underlying the EEG signal. The presence of non-linear dynamics can be determined by comparing the D-2 calculated from original EEG data with the D-2 from phase-randomized surrogate data. Methods: In a prospective study, we used this method in order to investigate non-linear structure in the EEG of Alzheimer patients and controls. Twenty-four patients (mean age 75.6 years) with 'probable Alzheimer's disease' (NINCDS-ADRDA criteria) and 22 controls (mean age 70.3 years) were examined. D-2 was calculated from original and surrogate data at 16 electrodes and in three conditions: with eyes open, eyes closed and during mental arithmetic. Results: D-2 was significantly lower in the Alzheimer patients compared to controls (P = 0.023). The difference between original and surrogate data was significant in both groups, implicating that non-linear dynamics play a role in the D-2 value. Moreover, this difference between original and surrogate data was smaller in the patient group. D-2 increased with activation, but not significantly more in controls than in patients. Conclusions: In conclusion, we found decreased dimensional complexity in the EEG of Alzheimer patients. This decrease seems to be attributable at least partially to different non-linear EEG dynamics. Because of this, non-linear EEG analysis could be a useful tool to increase our insight into brain dysfunction in Alzheimer's disease. (C) 1999 Elsevier Science Ireland Ltd. All rights reserved.
引用
收藏
页码:1159 / 1167
页数:9
相关论文
共 50 条
  • [1] A Pilot Study Investigating a Novel Non-Linear Measure of Eyes Open versus Eyes Closed EEG Synchronization in People with Alzheimer's Disease and Healthy Controls
    Blackburn, Daniel J.
    Zhao, Yifan
    De Marco, Matteo
    Bell, Simon M.
    He, Fei
    Wei, Hua-Liang
    Lawrence, Sarah
    Unwin, Zoe C.
    Blyth, Michelle
    Angel, Jenna
    Baster, Kathleen
    Farrow, Thomas F. D.
    Wilkinson, Iain D.
    Billings, Stephen A.
    Venneri, Annalena
    Sarrigiannis, Ptolemaios G.
    BRAIN SCIENCES, 2018, 8 (07):
  • [2] Non-linear dynamical analysis of the EEG in Alzheimer's disease with optimal embedding dimension
    Jeong, J
    Kim, SY
    Han, SH
    ELECTROENCEPHALOGRAPHY AND CLINICAL NEUROPHYSIOLOGY, 1998, 106 (03): : 220 - 228
  • [3] Quantifying synchrony patterns in the EEG of Alzheimer's patients with linear and non-linear connectivity markers
    Waser, Markus
    Garn, Heinrich
    Schmidt, Reinhold
    Benke, Thomas
    Dal-Bianco, Peter
    Ransmayr, Gerhard
    Schmidt, Helena
    Seiler, Stephan
    Sanin, Guenter
    Mayer, Florian
    Caravias, Georg
    Grossegger, Dieter
    Fruehwirt, Wolfgang
    Deistler, Manfred
    JOURNAL OF NEURAL TRANSMISSION, 2016, 123 (03) : 297 - 316
  • [4] Quantifying synchrony patterns in the EEG of Alzheimer’s patients with linear and non-linear connectivity markers
    Markus Waser
    Heinrich Garn
    Reinhold Schmidt
    Thomas Benke
    Peter Dal-Bianco
    Gerhard Ransmayr
    Helena Schmidt
    Stephan Seiler
    Günter Sanin
    Florian Mayer
    Georg Caravias
    Dieter Grossegger
    Wolfgang Frühwirt
    Manfred Deistler
    Journal of Neural Transmission, 2016, 123 : 297 - 316
  • [5] Usefulness of non-linear EEG analysis
    Micheloyannis, S
    Flitzanis, N
    Papanikolaou, E
    Bourkas, M
    Terzakis, D
    Arvanitis, S
    Stam, CJ
    ACTA NEUROLOGICA SCANDINAVICA, 1998, 97 (01): : 13 - 19
  • [6] Stochastic non-linear oscillator models of EEG: the Alzheimer's disease case
    Ghorbanian, Parham
    Ramakrishnan, Subramanian
    Ashrafiuon, Hashem
    FRONTIERS IN COMPUTATIONAL NEUROSCIENCE, 2015, 9
  • [7] Non-linear analysis of the sleep EEG
    Kobayashi, T
    Misaki, K
    Nakagawa, H
    Madokoro, S
    Ihara, H
    Tsuda, K
    Umezawa, Y
    Murayama, J
    Isaki, K
    PSYCHIATRY AND CLINICAL NEUROSCIENCES, 1999, 53 (02) : 159 - 161
  • [8] Use of Non-linear and Complexity features for EEG Based Dementia & Alzheimer disease Diagnosis
    Kulkarni, Nilesh. N.
    Parhad, Saurabh. V.
    Shaikh, Yasmin. P.
    2017 INTERNATIONAL CONFERENCE ON COMPUTING, COMMUNICATION, CONTROL AND AUTOMATION (ICCUBEA), 2017,
  • [9] Use of non-linear EEG measures to characterize EEG changes during mental activity
    Stam, CJ
    vanWoerkom, TCAM
    Pritchard, WS
    ELECTROENCEPHALOGRAPHY AND CLINICAL NEUROPHYSIOLOGY, 1996, 99 (03): : 214 - 224
  • [10] Non-linear EEG analysis in children with epilepsy and electrical status epilepticus during slow-wave sleep (ESES)
    Ferri, R
    Elia, M
    Musumeci, SA
    Stam, CJ
    CLINICAL NEUROPHYSIOLOGY, 2001, 112 (12) : 2274 - 2280