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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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“Instead of correlations, previous studies on mental health relied on the mutual information (MI) method from Information theory [87-89].”
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“The advantage of using the MI method is that the MI measures both linear and nonlinear statistical dependencies between variables.”
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Hukari, A., Cotroneo, S. F., & Salmelin, R. H. (2026) · Neuroinformatics · doi:10.1007/s12021-026-09784-3
“…(MEG), and a common application is the estimation of the similarity between signals recorded from different parts of the brain (Xu et al. 1997; Chen et al. 2000; Jeong et al. 2001; Na et al. 2002; Alonso et al. 2011; Bonita et al. 2014; Yin et al. 2017; Sun et al. 2018; Thuraisingham 2018).”
Li, Z., Wei, X., Zhou, T., Fu, X., Wang, Q., Zou, W., Gao, P., Wang, P., He, S., Yang, Z., et al. (2026) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2026.1928241
“When two signals are completely independent, their MI value is zero (Jeong et al., 2001).”
Sanchez-Bautista, J. A., Antelis, J. M., Hernández, L., & Mendoza-Montoya, O. (2026) · IEEE Access · doi:10.1109/access.2026.3737974
“Information-theoretic approaches, including Mutual Information (MI), assess statistical dependencies between signals and are sensitive to both linear and nonlinear relationships [12], [13].”
Melia, U. (2014) · doi:10.5821/dissertation-2117-95571 · Cited by 1
“, 2006), and furthermore in previous works mutual information functions were estimated with time series with length between 10(3) and 10(4) samples (Hoyer et al., 2005; Hoyer et al., 2006; Jeong et al., 2001).”
BackgroundSong, J. (2007) · Journal of Control Automation and Systems Engineering · doi:10.5302/j.icros.2007.13.9.838
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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