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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.”
Citations
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“One may obtain an asymmetric measure named timedelayed mutual information by introducing a time-lag parameter τ in any of the variables X and Y as follows [40,41]:”
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“38 There have been other studies 39,40 that used information theory with electroencephalography (EEG), magnetoencephalography (MEG), and functional MRI (fMRI) data.”
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“Importantly, this method detects linear and nonlinear statistical dependencies between time series, whereas cross-correlation is limited to the assessment of linear dependence (Jeong et al., 2001).”
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“It was shown that mutual information between distant electrodes is decreased in patients with AD which can be explained by a reduced information transmission among various cortical regions [19].”
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“We use time-shift cross mutual information [20] to measure the amount of information utilised by the prediction model.”
BackgroundMethodInfluentialA new algorithm for classification of ictal and pre-ictal epilepsy ECoG using MI and SVM
Chen, Z., Huang, L., Shen, Y., Wang, J., Zhao, R., & Dai, J. (2017) · IEEE International Conference on Signals and Systems (ICSigSys) · doi:10.1109/icsigsys.2017.7967043 · Cited by 10
“In these approaches, mutual information (MI) has emerged as a measurement of information flow or dynamical coupling between time series, because linear and nonlinear statistical dependencies among those series can be detected by MI. MI is often useful to analyze and detect brain diseases [1- 5], such as Alzheimer [1,2…”
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“Mutual information has been widely used in the neuroscience community (Borst and Theunissen, 1999; Chen et al., 2007; Jeong et al., 2001; Rozell and Johnson, 2005) and is seen as an informative measure of the statistical relationship between the stimulus and the response (Rozell and Johnson, 2005).”
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“Assuming that X and Y are two random variables, then their mutual information index I ( X ; Y ) is [42, 43] where H ( X ) is the entropy of the variable X , H ( X | Y ) is the relative entropy of X and Y , P XY is the joint probability of X and Y , P X is the probability of X , and P Y is the…”
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“The “complexity” or “irregularity” of the signal was also assessed by different measures stemming from information theory [12-20]: sample entropy [12,13], Tsallis entropy [14], approximate entropy [15,16], multiscale entropy [17], mutual information [18] and Lempel-Ziv complexity [19].”
MethodSelf-citationEntropy of brain rhythms: normal versus injury EEG
Thakor, N. V., Paul, J. S., Tong, S., Zhu, Y., & Bezerianos, A. (2002) · IEEE Statistical Signal Processing Workshop (SSP) · doi:10.1109/ssp.2001.955272 · Cited by 9
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“cross correlation [16], coherence [17], mutual information[18], partial directed coherence(PDC) [19], directed transfer”
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“For the source connectivity analysis, we calculated the time–frequency cross-MI (Jeong et al., 2001).”
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“A number of recent articles (Pezard et al. 1996; Roberts et al. 1998; Weber et al. 1998; Andino et al. 2000; Bhattacharya 2000; Bergey and Franaszczuk 2001; Jeong et al. 2001) have described the application of entropy and complexity measures to EEG and ERP data in order to differentiate cognitive tasks or clinical gro…”
BackgroundResearch on multi-dimensional N-back task induced EEG variations
Chen, R., Wang, X., Zhang, L., Yi, W., Ke, Y., Qi, H., He, F., Zhao, X. W., Wang, X., Ming, D., et al. (2015) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2015.7319554 · Cited by 8
“When color matching task was added, the CMI increased obviously, particularly over frontal, temporal and occipital areas.”
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“can be used to characterize a time series [25].”
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“These findings support the theory of brain reconfiguration in AD patients as the disease progresses, and have also been confirmed by connectivity studies [11, 12].”
ResultSelf-citationDirected information measures for assessing perceived audio quality using EEG
Mehta, K., & Kliewer, J. (2015) · Asilomar Conference on Signals, Systems and Computers (ACSSC) · doi:10.1109/acssc.2015.7421096 · Cited by 7
“This work was supported in part by NSF grant CCF-1065603.”
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“Further studies used approximate entropy, mutual entropy, multi-scale entropy, mutual information analysis, auto-mutual information and Tsallis entropy [5-10].”
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“28 Correlation, 29,30 coherence, 31 frequency ratio, 32 phase locking value (PLV), 33 mean phase coherence, 34 and mutual information 35,36 have been used to estimate functional interactions between multiple neural assemblies.”
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“AMI can reveal the information flow of the signal (Jeong et al. 2001).”
InfluentialSolving Reality Problems by Using Mutual Information Analysis
Liu, C.-J., Huang, C.-F., Huang, R.-Y., Shih, C.-S., Ho, M.-C., & Ho, H.-C. (2014) · Mathematical Problems in Engineering · doi:10.1155/2014/631706 · Cited by 6
“[8] also indicated that cross mutual information is a good way to assess the cortico-cortical connection between different cortical areas in AD patients.”
BackgroundData-driven evaluation of functional connectivity metrics
Zhang, Y., Han, J., Hu, X., Guo, L., & Liu, T. (2013) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2013.6556529 · Cited by 6
“Besides PeCo and PaCo, other functional connectivity metrics have been proposed in the literature, including WTC [11, 12] and MI [13].”
MethodAnalyzing dependence structure of the human brain in response to visual stimuli
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“Index Terms— Brain Connectivity, Dependence Measures, Electroencephalograhy (EEG), FIR Least-Square Filter, Generalized Measure of Association.”
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“In [17] different approximations of Gaussian mixture models are used to bound the MI transmission from stimulus to response in the cercal sensory system of a cricket.”
MethodCapturing Cognition via EEG-Based Functional Brain Networks
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“It has previously been employed as an analytical technique in many areas [1, 11, 21].”
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“This does not only supports the claim that there is a prominent reduction of information transmission between distant electrodes in AD [34], [35] but also suggests the same occurrence in VD. Similarly Fig.”
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“To estimate nonlinear interactions, numerous measures have been proposed, such as generalized synchronization (Stam and van Dijk, 2002), phase locking value (Lachaux et al., 1999), phase lag index (Stam et al., 2007), and mutual information (Jeong et al., 2001; Liu and Ioannides, 2006).”
BackgroundMutual information analysis on MCI-AD EEG signal during resting and task conditions
Nimmy, J. T., Puthankattil, S. D., & Menon, R. N. (2019) · IEEE Region 10 Conference (TENCON) · doi:10.1109/tencon.2019.8929242 · Cited by 5
“The transmission between different cortical areas is evaluated using CMI values calculated at the anterior, local posterior and distant brain sections [6].”
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“The detailed theory of AMI was presented in references [17,24,25].”
BackgroundMutual information in the frequency domain for the study of biological systems
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RetractedEEG-based Alzheimer’s disease detection: Past, present, and future
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“Examples include mutual information [2], [3] and Granger causality [4], [5].”
BackgroundMehta, K., & Kliewer, J. (2014) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2014.6854732 · Cited by 4
Use of Complexity Features for Diagnosis of Alzheimer Disease
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“276 The details of this mathematical derivation in the context of neuroscience is well explained in (36).”
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“a measure that gives the average bits that can be predicted in Xt given the state Xt−τ [21].”
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Moment to moment variability in functional brain networks during cognitive activity in EEG data
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“We have selected MI since it detects both linear and non-linear statistical dependencies (Jeong et al., 2001).”
MethodComparison of Methods for Early detection of Alzheimer's disease
Goh, C., Ifeachor, C., Camilleri, T. A., Latchoumane, C.-F. V., Bigan, C., Henderson, G. T., Fabri, S. G., Jeong, J., Hudson, N. V., Capotosto, P., et al. (2007) · Cited by 4
Self-citationNicolaou, N., & Nasuto, S. J. (2005) · Physical Review E · doi:10.1103/physreve.72.063901 · Cited by 4
Analyzing fatigue in dynamic exercise through electromyography signals and similarity metrics
Schiaber, P. D. S., Scalassara, P. R., Endo, W., Agulhari, C. M., Altimari, L. R., & Barbon, S. (2024) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2024.106864 · Cited by 3
Kang, Q., Li, Y., Li, X., Tian, M., Xiang, Y., Li, F., Peng, S., Xiong, Y., Yang, Y., Xiong, N. N., et al. (2025) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2025.3558834 · Cited by 3
“Among the nonlinear measures, mutual information (MI) is based on entropy theory and allows estimating linear and nonlinear coupling between time series [23].”
Fronto-parietal single-trial brain connectivity benefits successful memory recognition
Jun, S., Joo, Y. E., Sim, Y., Pyo, C., & Ham, K. (2022) · Translational Neuroscience · doi:10.1515/tnsci-2022-0265 · Cited by 3
“For the connectivity analysis, we calculated the time-frequency cross-MI [20].”
MethodTarik, F. B., Lao, Y., Hammood, M., Barnes, J., Mahanloo, M., Chrostowski, L., Khan, T. R., & Ryckman, J. D. (2025) · ACS Photonics · doi:10.1021/acsphotonics.5c02117 · Cited by 2
“Unlike conventional interferometers, which exhibit periodic spectra that shift predictably with applied voltage, the QCI produces an aperiodic spectrum that evolves deterministically but in a more complex and less predictable fashion.”
Yousefi, M. R., Kasmaei, B. S., Vahabie, A., Lucas, C., & Araabi, B. N. (2009) · Solar Physics · doi:10.1007/s11207-009-9418-6 · Cited by 3
On the application of the auto mutual information rate of decrease to biomedical signals
Escudero, J., Hornero, R., Abásolo, D., & Lopez, M. (2008) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/iembs.2008.4649616 · Cited by 3
“The statistical dependences between different brain regions have also been assessed computing the CMIF from pairs of electroencephalogram (EEG) channels in several brain states [11], [12] and neurological disorders, like Alzheimer’s disease [ 13 ] or schizophrenia [14].”
BackgroundMethodResultInfluentialAnde, S., Avasarala, S., Regatti, J. R., Pandey, N., Swain, S., Karunarathne, A., Giri, L., & Jana, S. (2021) · IEEE-EMBS Conference on Biomedical Engineering and Sciences · doi:10.1109/iecbes48179.2021.9398798 · Cited by 3
“ignored timing information and possible memory [7], [8].”
BackgroundThe Therapeutic Potential of Restoring Gamma Oscillations in Alzheimer’s Disease
Ferretti, A., Rogers-Healion, K., & Fotros, A. (2022) · Advances in Psychiatry and Behavioral Health · doi:10.1016/j.ypsc.2022.05.002 · Cited by 3
Analysis of Spectral Entropy and Maximum Power of EEG as Authentication Mechanisms
Vulpe, A., Zamfirache, M., & Caranica, A. (2023) · International Conference on Speech Technology and Human-Computer Dialogue (SpeD) · doi:10.1109/sped59241.2023.10314890 · Cited by 3
“Results depend on technicians' knowledge and experience, and the conventional clinical method of observing waveform shapes is considered subjective[1].”
BackgroundFunctional Connectivity Analysis of FMRI Time-Series Data
Zhou, D. (2010) · D-Scholarship@Pitt (University of Pittsburgh) · Cited by 3
Mathematical Issues in the Inference of Causal Interactions among Multichannel Neural Signals
Jung, Y., Kim, K. H., & Im, C. (2011) · Journal of Applied Mathematics · doi:10.1155/2012/472036 · Cited by 3
Quantifying cognitive state from EEG using phase synchrony
Wan, L., Fadlallah, B. H., Keil, A., & Príncipe, J. C. (2013) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2013.6610872 · Cited by 3
“The employed measures of dependence include linear measures such as cross-correlation [5], [6] or nonlinear measures suchasmutual information [7], [8], besides a recently introduced measure that was termed generalized measure of”
Comparison of predictive contract mechanisms from an information theory perspective
Zhang, X., Ward, T. E., & McLoone, S. (2012) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/2168996.2168998 · Cited by 3
Mutual Information based Relevance Network Analysis: A Parkinson's Disease Study
Lee, P. W.-H., Wang, Z. J., & McKeown, M. J. (2008) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2008.4517655 · Cited by 3
“Previously, MI has been applied to Alzheimer’s [3], Schizophrenic [4], and Odor Stimulation [5] data to assess information transmission between different parts of the brain and it’s connectivity.”
MethodEffect of age on changes in motor units functional connectivity
Arjunan, S. P., & Kumar, D. (2015) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2015.7318998 · Cited by 3
“The predominant loss of type II fibres in the muscle [15][16] is also accompanied with the change in the recruitment pattern of the motor units of the muscle with an increase in motor unit (MU) size [9] [17].”
BackgroundA novel methodology to quantify dense EEG in cognitive tasks
Silva, C. S., Príncipe, J. C., & Keil, A. (2017) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2017.7952595 · Cited by 3
“In terms of measures of functional connectivity, the correlation coefficient [2][3] will only capture linear interactions between time series, whereas other measures, such as mutual information [3][4][5][6][7], time-series generalized measure of association (TGMA) [8], phase synchronization [3][9][10], copulas family…”
BackgroundThe Role of Neuroimaging and Electroencephalogram in Diagnosis of Alzheimer Disease
N., N., P., P., & V., V. (2017) · International Journal of Computer Applications · doi:10.5120/ijca2017914435 · Cited by 3
Connectivity network analysis of EEG signals for detecting deception
Xiong, Y., Gao, J., & Chen, R. (2019) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1176/3/032051 · Cited by 3
“Jeong et al. [12] and liu et al. [13] investigated Alzheimer's disease using MI method.”
MethodMutual Information for the Detection of Crush
Harding, P., Amos, M., & Gwynne, S. (2011) · Springer eBooks · doi:10.1007/978-1-4419-9725-8_73 · Cited by 3
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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