The DOI matches its Crossref record; 57 of the 59 references it cites were found in a registry or an index.
Mutual information analysis of the EEG in patients with Alzheimer's disease
Abstract
Objective Mutual information provides a measure of both the linear and nonlinear statistical dependencies between two time series. Cross-mutual information (CMI) is used to quantify the information transmitted from one time series to another, while auto mutual information (AMI) in a time series estimates how much on average the value of the time series can be predicted from values of the time series at preceding points. The aim of this study is to assess information transmission between different cortical areas in Alzheimer's disease (AD) patients by estimating the average CMI between EEG electrodes.Methods We recorded the EEG from 16 scale electrodes in 15 AD patients and 15 age-matched normal controls, and estimated the local, distant, and interhemispheric CMIs of the EEG in both groups. The rate of decrease (with increasing delay) of the AMI of the EEG was also measured to evaluate the complexity of the EEG in AD patients.Results The local CMI in AD subjects was lower than that in normal controls, especially over frontal and antero-temporal regions. A prominent decrease in information transmission between distant electrodes in the right hemisphere and between corresponding interhemispheric electrodes was detected in the AD patients. In addition, the AMIs throughout the cerebrums of the AD patients decreased significantly more slowly with delay than did the AMIs of normal controls.Conclusions These results are consistent with previous findings that suggest the association of EEG abnormalities in AD patients with functional impairment of information transmission in long cortico-cortical connections.
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In the citing paper
“Instead of correlations, previous studies on mental health relied on the mutual information (MI) method from Information theory [87-89].”
In the citing paper
“The advantage of using the MI method is that the MI measures both linear and nonlinear statistical dependencies between variables.”
Citations
Cites
A Mathematical Theory of Communication
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The Mathematical Theory of Communication
Shannon, C. E., Weaver, W., & Wiener, N. (1950) · Physics Today · doi:10.1063/1.3067010 · Cited by 9,885
Independent coordinates for strange attractors from mutual information
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Research works printed but not linked: 1
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Cited by
Sorted by citations
Nonlinear dynamical analysis of EEG and MEG: Review of an emerging field
Stam, C. J. (2005) · Clinical Neurophysiology · doi:10.1016/j.clinph.2005.06.011 · Cited by 1,410
“A few years later it was shown that a loss of dynamical complexity can already be demonstrated in Alzheimer patients in an eyes-closed resting state (Besthorn et al., 1995; Jelles et al., 1999a; Jeong et al., 1998c, 2001a; Stam et al., 1994, 1995; Yagyu et al., 1997b).”
BackgroundEEG dynamics in patients with Alzheimer's disease
Jeong, J. (2004) · Clinical Neurophysiology · doi:10.1016/j.clinph.2004.01.001 · Cited by 1,400
“A number of studies failed to find any significant difference of EEG measures in between AD and VaD (Ettlin et al., 1989; Robinson et al., 1994; Soininen et al., 1982).”
BackgroundMethodInfluentialSelf-citationNonlinear multivariate analysis of neurophysiological signals
Pereda, E., Quiroga, R. Q., & Bhattacharya, J. (2005) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2005.10.003 · Cited by 1,066
“A similar result was found in patients with Alzheimer’s disease, mainly in the frontal and anteriortemporal regions (Jeong et al., 2001).”
ResultGraph theoretical analysis of magnetoencephalographic functional connectivity in Alzheimer's disease
Stam, C. J., de Haan, W., Daffertshofer, A., Jones, B. F., Manshanden, I., van Walsum, A. M. V. C., Montez, T., Verbunt, J. P. A., de Munck, J. C., van Dijk, B. W., et al. (2008) · Brain · doi:10.1093/brain/awn262 · Cited by 922
“BRAIN”
Review of advanced techniques for the estimation of brain connectivity measured with EEG/MEG
Sakkalis, V. (2011) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2011.06.020 · Cited by 637
“In addition, CMI when applied in 15 AD patients and age-matched normal controls was successful in identifying EEG abnormalities in AD patients with functional impairment of information transmission in long cortico–cortical connections [49].”
BackgroundMethodInfluential
Metadata
- Authors
- Jeong, J., Gore, J. C., & Peterson, B. S.
- Affiliations
- Yale University (US)
- Corresponding author
- Jeong, Jaeseung
- Venue
- Clinical Neurophysiology
- Publisher
- Elsevier BV
- Published
- 2001-05
- Volume
- 112
- Issue
- 5
- Pages
- 827-835
- ISSN
- 1388-2457 (ISSN-L)
- OpenAlex
- W2003689160
- Cited by
- 397
- References
- 59
- Citations by year
- 2026: 10 · 2025: 12 · 2024: 10 · 2023: 13 · 2022: 22 · 2021: 23 · 2020: 16 · 2019: 28 · 2018: 23 · 2017: 17 · 2016: 20 · 2015: 29 · 2014: 21 · 2013: 12 · 2012: 15
- FWCI
- 2.79
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Clinical Neurophysiology (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Tourette Association of America
- National Institutes of Health
- National Institute of Mental Health (MH-01232, K08MH001232)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Keywords
- Alzheimer's disease, EEG, mutual information, information transmission, interhemispheric connectivity, corticocortical connections, EEG complexity
- MeSH terms
- Aged, Alzheimer Disease, Brain, Brain Mapping, Electroencephalography, Female, Humans, Male, Nerve Net, Predictive Value of Tests, Probability, Reference Values, Reproducibility of Results
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