Sugihara causality analysis of scalp EEG for detection of early Alzheimer's disease

Joseph C. McBride, Xiaopeng Zhao, Nancy B. Munro, Gregory A. Jicha, Frederick A. Schmitt, Richard J. Kryscio, Charles D. Smith, Yang Jiang

Research output: Contribution to journalArticlepeer-review

46 Scopus citations


Recently, Sugihara proposed an innovative causality concept, which, in contrast to statistical predictability in Granger sense, characterizes underlying deterministic causation of the system. This work exploits Sugihara causality analysis to develop novel EEG biomarkers for discriminating normal aging from mild cognitive impairment (MCI) and early Alzheimer's disease (AD). The hypothesis of this work is that scalp EEG based causality measurements have different distributions for different cognitive groups and hence the causality measurements can be used to distinguish between NC, MCI, and AD participants. The current results are based on 30-channel resting EEG records from 48 age-matched participants (mean age 75.7 years) - 15 normal controls (NCs), 16 MCI, and 17 early-stage AD. First, a reconstruction model is developed for each EEG channel, which predicts the signal in the current channel using data of the other 29 channels. The reconstruction model of the target channel is trained using NC, MCI, or AD records to generate an NC-, MCI-, or AD-specific model, respectively. To avoid over fitting, the training is based on the leave-one-out principle. Sugihara causality between the channels is described by a quality score based on comparison between the reconstructed signal and the original signal. The quality scores are studied for their potential as biomarkers to distinguish between the different cognitive groups. First, the dimension of the quality scores is reduced to two principal components. Then, a three-way classification based on the principal components is conducted. Accuracies of 95.8%, 95.8%, and 97.9% are achieved for resting eyes open, counting eyes closed, and resting eyes closed protocols, respectively. This work presents a novel application of Sugihara causality analysis to capture characteristic changes in EEG activity due to cognitive deficits. The developed method has excellent potential as individualized biomarkers in the detection of pathophysiological changes in early-stage AD.

Original languageEnglish
Pages (from-to)258-265
Number of pages8
JournalNeuroImage: Clinical
StatePublished - 2015

Bibliographical note

Funding Information:
Research was sponsored in part by the Laboratory Directed Research and Development Program of Oak Ridge National Laboratory , managed by UT-Battelle, LLC, for the US Department of Energy under Contract No. DE-AC05-00OR22725 ; by the NSF under grant numbers CMMI-0845753 and CMMI-1234155 ; and in part by the NIH under grants NIH P30 AG028383 to the UK Sanders-Brown Center on Aging, NIH AG00986 to YJ, and NIH NCRR UL1TR000117 to the UK Center for Clinical and Translational Science. The contribution to this paper by N.B. Munro was prepared while acting in her own independent capacities and not on behalf of UT-Battelle, LLC, or its affiliates or successors, or Oak Ridge National Laboratory, or the US Department of Energy. We deeply thank Dr. David Wekstein of the UK Alzheimer's Research Center for his key role in getting the collaboration between ORNL and UK in place to make the pilot study possible. We thank A. Lawson, E. Walsh, J. Lianekhammy, S. Kaiser, C. Black, K. Tran, and L. Broster at the University of Kentucky for their assistance in data acquisition and database management, and E. Abner at the Biostatistics Core at the UK Aging Center for providing the mini–mental state examination scores of some participants.

Publisher Copyright:
© 2014 The Authors. Published by Elsevier Inc.


  • Causality analysis
  • EEG-based diagnosis
  • Early Alzheimer's disease
  • Mild cognitive impairment

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging
  • Neurology
  • Clinical Neurology
  • Cognitive Neuroscience


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