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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
Cites
A Mathematical Theory of Communication
Shannon, C. E. (1948) · Bell System Technical Journal · doi:10.1002/j.1538-7305.1948.tb01338.x · Cited by 88,486
A Mathematical Theory of Communication
Shannon, C. E. (1948) · The MIT Press eBooks · doi:10.1002/j.1538-7305.1948.tb00917.x · Cited by 50,387
Elements of Information Theory
Cover, T. M., & Thomas, J. A. (2001) · Wiley eBooks · doi:10.1002/0471200611 · Cited by 37,139
Hamilton, M. (1960) · Journal of Neurology, Neurosurgery & Psychiatry · doi:10.1136/jnnp.23.1.56 · Cited by 32,817
McKhann, G., Drachman, D., Folstein, M., Katzman, R., Price, D., & Stadlan, E. M. (1984) · Neurology · doi:10.1212/wnl.34.7.939 · Cited by 27,915
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
Fraser, A. M., & Swinney, H. L. (1986) · Physical Review A · doi:10.1103/physreva.33.1134 · Cited by 4,665
Gray, C. M., König, P., Engel, A. K., & Singer, W. (1989) · Nature · doi:10.1038/338334a0 · Cited by 4,183
Electroencephalography—Basic principles, clinical applications and related fields
(1982) · Journal of the Autonomic Nervous System · doi:10.1016/0165-1838(82)90067-4 · Cited by 3,449
Elements of Information Theory
Jacobs, K. (1992) · Birkhäuser Basel eBooks · doi:10.1007/978-3-0348-8645-1_7 · Cited by 2,957
Research works printed but not linked: 1
- Kwon, 1986, Korean version of mini-mental state examination (MMSE-K). part I: development of the test for the elderly, J Korean Neuropsychiat Assoc, 28, 125
Cited by
Sorted by citations
Items 101–200 of 397
Liang, T., Zhang, Q., Liu, X., Lou, C., Liu, X., & Wang, H. (2020) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2020.3028199 · Cited by 40
“nerve signals [25], [26].”
A Novel Segmentation, Mutual Information Network Framework for EEG Analysis of Motor Tasks
Wang, Z., Lee, P. L., & McKeown, M. J. (2009) · BioMedical Engineering OnLine · doi:10.1186/1475-925x-8-9 · Cited by 37
“Previously, researchers have utilized MI as a suitable metric to investigate EEG coupling in various pathological conditions [9-11].”
BackgroundApplied Strategies Towards EEG/MEG Biomarker Identification in Clinical and Cognitive Research
Sakkalis, V. (2011) · Biomarkers in Medicine · doi:10.2217/bmm.10.121 · Cited by 38
The application of information theory for the research of aging and aging-related diseases
Blokh, D., & Stambler, I. (2016) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2016.03.005 · Cited by 37
“Such conclusions on the association of diminished heart rate variability (complexity) with heart disease were reached by a large number of authors, regarding a variety of heart conditions, such as ischemic heart disease, including coronary artery disease (CAD) and acute myocardial infarction (AMI), and other condition…”
BackgroundInfluentialOn the Early Diagnosis of Alzheimer’s Disease from EEG Signals: A Mini-Review
Dauwels, J., Vialatte, F., & Cichocki, A. S. (2010) · Springer eBooks · doi:10.1007/978-90-481-9695-1_106 · Cited by 37
“In particular, a common hypothesis is that basal forebrain neurons may be severely affected in AD, resulting in a cerebral cholinergic deficit that leads to memory loss and other cognitive symptoms [58].”
BackgroundConditional correlation as a measure of mediated interactivity in fMRI and MEG/EEG
Marrelec, G., Jean Daunizeau, A., Pélégrini‐Issac, M., Doyon, J., & Benali, H. (2005) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2005.853211 · Cited by 38
Mutual Information Analysis of Sleep EEG in Detecting Psycho-Physiological Insomnia
Aydın, S., Tunga, M. A., & Yetki̇n, S. (2015) · Journal of Medical Systems · doi:10.1007/s10916-015-0219-1 · Cited by 36
Simons, S., & Abásolo, D. (2017) · Entropy · doi:10.3390/e19030129 · Cited by 36
“This method has been interpreted as a measure of the degree of complexity in time series [34], so finding significant differences at T6 would be in agreement with the interpretation of dLZC as a measure of complexity for pairs of signals.”
MethodResultInfluentialAnalysis of long range dependence in the EEG signals of Alzheimer patients
John, T. N., Puthankattil, S. D., & Menon, R. N. (2018) · Cognitive Neurodynamics · doi:10.1007/s11571-017-9467-8 · Cited by 37
“This reduction in information transmission could be due to impairment of cortico-cortical fibers and neuronal death in the brain as reported by Jeong et al. (2001) and Locatelli et al. (1998).”
BackgroundMethodResultInfluentialCoronel, C., Garn, H., Waser, M., Deistler, M., Benke, T., Dal‐Bianco, P., Ransmayr, G. N., Seiler, S., Grossegger, D., & Schmidt, R. (2017) · Entropy · doi:10.3390/e19030130 · Cited by 36
“This loss of complexity was previously investigated in [9,10].”
BackgroundResultSamani, A., Srinivasan, D., Mathiassen, S. E., & Madeleine, P. (2016) · Experimental Brain Research · doi:10.1007/s00221-016-4798-y · Cited by 34
“Functional connectivity quantifies the level of shared information between a muscle pair (Jeong et al. 2001).”
BackgroundCosta, J., González, H. A., Valldeoriola, F., Gaig, C., Tolosa, E., & Valls‐Solé, J. (2010) · Movement Disorders · doi:10.1002/mds.23334 · Cited by 33
“can be predicted from values of the same time series at preceding points.(35,36)”
BackgroundMusical experience sharpens human cochlear tuning
Bidelman, G. M., Nelms, C. E., & Bhagat, S. P. (2016) · Hearing Research · doi:10.1016/j.heares.2016.02.012 · Cited by 34
“MI offers a means to detect both linear and nonlinear statistical dependencies between responses and is preferable here over correlations, which measure only linear dependencies (Jeong et al., 2001).”
BackgroundWaser, M., Benke, T., Dal‐Bianco, P., Garn, H., Mosbacher, J. A., Ransmayr, G. N., Schmidt, R., Seiler, S., Sørensen, H. B. D., & Jennum, P. (2018) · Brain and Behavior · doi:10.1002/brb3.1197 · Cited by 34
Samani, A., Pontonnier, C., Dumont, G., & Madeleine, P. (2015) · PLoS ONE · doi:10.1371/journal.pone.0116211 · Cited by 31
“established and has a broad application in fields such as image processing [24] and biomedical signal processing [23].”
BackgroundMethodCortical functional activity in patients with generalized anxiety disorder
Wang, Y., Chai, F., Zhang, H., Liu, X., Xie, P., Zheng, L., Yang, L., Li, L., & Fang, D. (2016) · BMC Psychiatry · doi:10.1186/s12888-016-0917-3 · Cited by 30
Jin, S., Jeong, W., Lee, D.-S., Jeon, B. S., & Chung, C. K. (2014) · Journal of Neurophysiology · doi:10.1152/jn.00585.2013 · Cited by 29
Abazid, M., Houmani, N., Boudy, J., Dorizzi, B., Mariani, J., & Kinugawa, K. (2021) · Entropy · doi:10.3390/e23111553 · Cited by 28
“In that sense, alternative methods were used for assessing the inter-channel relations, such as mutual information [28,29], coherence [29–31], Granger causality [29,32] and phase lag index [33,34].”
BackgroundMethodMIC as an Appropriate Method to Construct the Brain Functional Network
Zhang, Z., Sun, S., Yi, M., Wu, X., & Ding, Y. (2015) · BioMed Research International · doi:10.1155/2015/825136 · Cited by 28
Pargett, M., & Umulis, D. M. (2013) · Methods · doi:10.1016/j.ymeth.2013.03.024 · Cited by 28
Detrended fluctuation analysis of human brain electroencephalogram
Pan, C., Zheng, B., Wu, Y., Wang, Y., & Tang, X. (2004) · Physics Letters A · doi:10.1016/j.physleta.2004.06.070 · Cited by 28
Güdücü, Ç., Olcay, B. O., Schäfer, L., Aziz, M., Schriever, V. A., Özgören, M., & Hummel, T. R. (2018) · Brain Research · doi:10.1016/j.brainres.2018.12.012 · Cited by 28
Zubler, F., Steimer, A., Gast, H., & Schindler, K. A. (2014) · International review of neurobiology · doi:10.1016/b978-0-12-418693-4.00008-x · Cited by 27
Dong, W., Li, P., Zhou, Y., Sun, Y., Xu, J. B., Liu, Y., Xiaoli Li, A., Li, J., Bian, Z., & Wang, L. (2020) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2020.3004462 · Cited by 27
Escudero, J., Hornero, R., Poza, J., Abásolo, D., & Fernández, A. (2008) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2008.01.001 · Cited by 27
“Moreover, the AD patients' EEG and MEG data have also been studied with various non-linear techniques [5,6,17,20] like complexity estimators [21–23] or connectivity measures [24,25].”
Ruiz-Gómez, S. J., Gómez, C., Poza, J., Martínez‐Zarzuela, M., Tola-Arribas, M. Á., Cano, M., & Hornero, R. (2018) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2018.00076 · Cited by 26
Synchrony and Complexity in State-Related EEG Networks: An Application of Spectral Graph Theory
Ghaderi, A. H., Baltaretu, B. R., Andevari, M. N., Bharmauria, V., & Balcı, F. (2020) · Neural Computation · doi:10.1162/neco_a_01327 · Cited by 28
“The transfer entropy (Wollstadt, Martínez-Zarzuela, Vicente, Díaz-Pernas, & Wibral, 2014) and the mutual information (Jeong, Gore, & Peterson, 2001) are appropriate measures to evaluate the level of disorder between two signals.”
BackgroundYu, H., Lei, X., Song, Z., Wang, J., Wei, X., & Yu, B. (2018) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2018.05.009 · Cited by 27
Ehteshamzad, S. (2024) · Journal of Alzheimer s Disease Reports · doi:10.3233/adr-230159 · Cited by 25
Kawczyński, A., Samani, A., Mroczek, D., Chmura, P., Błach, W., Migasiewicz, J., Klich, S., Chmura, J., & Madeleine, P. (2015) · European Journal of Applied Physiology · doi:10.1007/s00421-015-3114-z · Cited by 25
“Given a measurement xi from the EMG set X = {xi}, and yj from the EMG setY = {yj} representing a muscle pair, mutual information is defined as (Jeong et al. 2001): where pXY(xi, yj) is the joint probability density for the EMG measurements of X and Y. Mutual information was estimated over 250 ms…”
BackgroundInfluentialCombrisson, E., Allegra, M., Basanisi, R., Ince, R. A. A., Giordano, B. L., Bastin, J., & Brovelli, A. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119347 · Cited by 31
Na, S. H., Jin, S., & Kim, S. Y. (2006) · International Journal of Psychophysiology · doi:10.1016/j.ijpsycho.2006.03.006 · Cited by 25
Madeleine, P., Xie, Y., Szeto, G. P. Y., & Samani, A. (2016) · Clinical Neurophysiology · doi:10.1016/j.clinph.2016.06.015 · Cited by 24
Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease
Ryu, J., Vero, J., Dobkin, R. D., & Torres, E. B. (2019) · Journal of Visualized Experiments · doi:10.3791/59827 · Cited by 26
Samani, A., Srinivasan, D., Mathiassen, S. E., & Madeleine, P. (2015) · Journal of Electromyography and Kinesiology · doi:10.1016/j.jelekin.2015.01.005 · Cited by 24
“The NMI is a measure of the amount of information shared between two time series (Jeong et al., 2001).”
BackgroundInfluentialHuang, C., Zhao, C.-G., & Hwang, I. (2014) · Behavioural Brain Research · doi:10.1016/j.bbr.2014.07.054 · Cited by 24
Lee, T., Yu, Y. W. ‐., Hong, C., Tsai, S., Wu, H., & Chen, T. (2011) · Brain Research · doi:10.1016/j.brainres.2010.12.073 · Cited by 26
Dimensional Information-Theoretic Measurement of Facial Emotion Expressions in Schizophrenia
Hamm, J., Pinkham, A. E., Gur, R. C., Verma, R., & Kohler, C. G. (2014) · Schizophrenia Research and Treatment · doi:10.1155/2014/243907 · Cited by 24
“This method can be applied for delineating dynamic emotional expressivity in healthy and clinical populations.”
Normalised Mutual Information of High-Density Surface Electromyography during Muscle Fatigue
Bingham, A., Arjunan, S. P., Jelfs, B., & Kumar, D. (2017) · Entropy · doi:10.3390/e19120697 · Cited by 24
Chang, Q., Liu, M., Tian, Q., Wang, H., Luo, Y., Zhang, J., & Wang, C. (2019) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2019.00379 · Cited by 24
Ramanand, P., Bruce, M. C., & Bruce, E. N. (2010) · Journal of Clinical Neurophysiology · doi:10.1097/wnp.0b013e3181eaa9f5 · Cited by 24
“To study the information transfer across brain regions, MI was applied to EEGs from normal subjects and from subjects suffering various pathological conditions (Jeong et al. 2001; Na et al. 2002).”
MethodQuantifying Cognitive State From EEG Using Dependence Measures
Fadlallah, B. H., Seth, S., Keil, A., & Príncipe, J. C. (2012) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2012.2210283 · Cited by 23
“Often electrical activity is measured over time across several cortical areas, and the standard practice quantifies the association of the signals in terms of correlation [25]–[28] or mutual information (MI) [29]–[31].”
BackgroundStable functional networks exhibit consistent timing in the human brain
Chapeton, J. I., Inati, S. K., & Zaghloul, K. A. (2016) · Brain · doi:10.1093/brain/aww337 · Cited by 24
“…we use a coupling measure based on time-lagged mutual information, which captures both linear and non-linear statistical relationships between two brain regions (Varma et al., 1997; Chen et al., 2000; Jeong et al., 2001; Na et al., 2002; Cho et al., 2012; Lenne et al., 2013; Nigam et al., 2016).”
MethodJin, S., Kwon, Y., Jeong, J.-S., Kwon, S. W., & Shin, D.-H. (2006) · International Journal of Psychophysiology · doi:10.1016/j.ijpsycho.2006.06.003 · Cited by 23
“Although A-CMI does not directly estimate axonal connection or cortico-cortical communication, it is possible to quantify the transmission of information statistically from one site in a time series to another (Jeong et al., 2001).”
BackgroundMethodSelf-citationSubramaniyam, N. P., Donner, R. V., Caron, D., Panuccio, G., & Hyttinen, J. (2021) · Nonlinear Dynamics · doi:10.1007/s11071-021-06610-0 · Cited by 22
Waser, M., Garn, H., Schmidt, R., Benke, T., Dal‐Bianco, P., Ransmayr, G. N., Schmidt, H., Seiler, S., Sanin, G., Mayer, F., et al. (2015) · Journal of Neural Transmission · doi:10.1007/s00702-015-1461-x · Cited by 22
“…and directed transfer functions in Dauwels et al. (2010a) and Babiloni et al. (2009), information-theoretic measures such as mutual information in Jeong et al. (2001), Wan et al. (2008), and Dauwels et al. (2010a), phase synchrony measures in Stam et al. (2003), Pijnenburg et al. (2004), Stam et…”
BackgroundOmmundsen, N., Engedal, K., & Øksengård, A. R. (2011) · Dementia and Geriatric Cognitive Disorders · doi:10.1159/000324878 · Cited by 22
Özçoban, M. A., Tan, O., Aydın, S., & Akan, A. (2017) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-017-1689-8 · Cited by 22
“Mutual information (MI) [18], coherence function [44] and cross correlation [42]”
BackgroundLiang, T., Zhang, Q., Liu, X., Dong, B., Liu, X., & Wang, H. (2021) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/s12984-021-00872-w · Cited by 21
Wen, D., Li, R., Tang, H., Liu, Y., Wan, X., Dong, X., Saripan, M. I., Lan, X., Song, H., & Zhou, Y. (2022) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2022.3166224 · Cited by 21
Dynamical interactions reconfigure the gradient of cortical timescales
Sorrentino, P., Rabuffo, G., Baselice, F., Lopez, E. T., Liparoti, M., Quarantelli, M., Sorrentino, G., Bernard, C., & Jirsa, V. (2022) · Network Neuroscience · doi:10.1162/netn_a_00270 · Cited by 18
“According to previous works on M/EEG (Gómez et al., 2007; Jeong et al., 2001), a stable estimate of the probability distribution of any real-valued signal X(t ) is obtained by dividing the data into 64 bins.”
ResultApproximate Entropy of EEG Background Activity in Alzheimer's Disease Patients
Abásolo, D., Hornero, R., & Espino, P. (2009) · Surrey Research Insight Open Access (The University of Surrey) · doi:10.1080/10798587.2009.10643051 · Cited by 21
“For instance, mutual information analysis [13] and synchronization likelihood [14], [15] have been used to assess information transmission between different cortical areas in AD.”
MethodHemispheric asymmetry in non‐linear interdependence of EEG in post‐traumatic stress disorder
Kim, J., Chae, J., Ko, H., Latchoumane, C.-F. V., Banerjee, A., Mandell, D. J., Hoven, C. W., & Jeong, J. (2012) · Psychiatry and Clinical Neurosciences · doi:10.1111/j.1440-1819.2011.02300.x · Cited by 21
“Mutual information has been applied, as a non-linear information transmission measure, to EEG in Alzheimer’s disease and has demonstrated reduced information transmission among interhemispheric regions.(17) Mutual information has also been applied to the EEG in schizophrenia(18) and in sleep deprivation.”
BackgroundSelf-citationTun, N. N., Sanuki, F., & Iramina, K. (2021) · Sensors · doi:10.3390/s21134380 · Cited by 20
“From the perspective of nonlinear correlation analysis, mutual information can provide a measure of nonlinear dependency between two signals [31].”
BackgroundMelia, U. S. P., Vallverdú, M., Borrat, X., Valencia, J. F., Jospin, M., Jensen, E. W., Gambús, P. L., & Caminal, P. (2015) · PLoS ONE · doi:10.1371/journal.pone.0123464 · Cited by 20
“Furthermore, in other studies mutual information functions were estimated on EEG and heart beat time series with length between 10 and 10 samples [60, 61].”
BackgroundMeasuring topological descriptors of complex networks under uncertainty
Raimondo, S., & Domenico, M. D. (2021) · Physical review. E · doi:10.1103/physreve.103.022311 · Cited by 21
Weighted Brain Network Analysis on Different Stages of Clinical Cognitive Decline
Abazid, M., Houmani, N., Dorizzi, B., Boudy, J., Mariani, J., & Kinugawa, K. (2022) · Bioengineering · doi:10.3390/bioengineering9020062 · Cited by 20
“Other studies investigated functional connectivity between electrode pairs [24,25,33,34] to assess the degree of signal synchronization between different brain areas, using a large variety of measures, such as the phase-lag index [35,36], coherence [37–40], mutual information [41] and synchronization likelihood [42,43…”
BackgroundResting-state electroencephalographic characteristics related to mild cognitive impairments
Kim, S., Shin, C., Yim, J., Seo, K., Ryu, H. B., Choi, H., Park, J., & Min, B. (2023) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2023.1231861 · Cited by 20
Wu, M., Ouyang, R., Zhou, C., Sun, Z., Li, F., & Li, P. (2024) · Frontiers in Neuroscience · doi:10.3389/fnins.2023.1345770 · Cited by 19
Wang, R., Wang, L., Yang, Y., Li, J., Wu, Y., & Lin, P. (2016) · Physical review. E · doi:10.1103/physreve.94.052411 · Cited by 19
Fedorowich, L. M., & Côté, J. N. (2018) · Applied Ergonomics · doi:10.1016/j.apergo.2018.05.009 · Cited by 19
Si, S., Wang, B., Liu, X., Yu, C., Ding, C., & Hai, Z. (2019) · Entropy · doi:10.3390/e21030300 · Cited by 21
“Moreover, previous studies have found that MI can be used to quantify the effect of correlations between the Mini-mental state examination (MMSE) scores of AD and cognitive stage [37,38].”
BackgroundPermutation auto-mutual information of electroencephalogram in anesthesia
Liang, Z., Wang, Y., Ouyang, G., Voss, L. J., SLEIGH, J. W., & Xiaoli Li, A. (2013) · Journal of Neural Engineering · doi:10.1088/1741-2560/10/2/026004 · Cited by 19
“This can estimate a degree x(t + τ ) that can be predicted from x(t) (Jeong et al 2001).”
BackgroundAbásolo, D., Hornero, R., Espino, P., Escudero, J., & Gómez, C. (2007) · Conference proceedings · doi:10.1109/iembs.2007.4353769 · Cited by 19
“Some authors have chosen to quantify complexity with the AMI rate of decrease [13].”
BackgroundMethodResultInfluentialDiagnosis of Alzheimer Disease using EEG Signals
Kulkarni, N. N., & Bairagi, V. K. (2014) · Cited by 19
Rel-CNN: Learning Relationship Features in Time Series for Classification
He, F., Fu, T.-Y., & Lee, W.-C. (2022) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2022.3186963 · Cited by 18
“…of a time series, e.g., local shapelets [7], while the latter refers to some intrinsic properties or patterns in time series observable only by looking over the full interval of a time series, e.g., periodicity [8], long-term trend [9], entropy [10], stationary [11] and autocorrelation [12].”
BackgroundCross-conditional entropy and coherence analysis of pharmaco-EEG changes induced by alprazolam
Alonso, J. F., Mañanas, M. Á., Romero, S., Rojas-Martínez, M., & Jordi Riba, A. (2011) · Psychopharmacology · doi:10.1007/s00213-011-2587-7 · Cited by 18
Olcay, B. O., & Karaçalı, B. (2019) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2019.103441 · Cited by 18
Modes of information flow in collective cohesion
Sattari, S., Basak, U. S., James, R. G., Perrin, L. W., Crutchfield, J. P., & Komatsuzaki, T. (2022) · Science Advances · doi:10.1126/sciadv.abj1720 · Cited by 19
“Nonetheless, estimating two-agent information measures has proven useful for monitoring influence in systems having more than two agents [5, 13, 14, 21, 23].”
BackgroundNeurocontrol: Methods, models and technologies for manipulating dynamics in the brain
Ritt, J. T., & Ching, S. (2015) · Proceedings of the ... American Control Conference/Proceedings of the American Control Conference · doi:10.1109/acc.2015.7171915 · Cited by 16
“MI quantifies the shared information between two signals and unlike correlation can identify nonlinear dependencies [132], [133].”
BackgroundAn Introduction to MEG Connectivity Measurements
Brookes, M. J., Woolrich, M. W., & Price, D. (2014) · Springer eBooks · doi:10.1007/978-3-642-33045-2_16 · Cited by 17
Universal Cointegration and Its Applications
Tu, C., Fan, Y., & Fan, J. (2019) · iScience · doi:10.1016/j.isci.2019.08.048 · Cited by 17
Lee, T., Yu, Y. W. ‐., Hong, C., Tsai, S., Wu, H., & Chen, T. (2012) · Brain Topography · doi:10.1007/s10548-012-0229-y · Cited by 16
EEG based functional connectivity analysis of human pain empathy towards humans and robots
Chang, W., Wang, H., Yan, G., Lü, Z., Liu, C., & Hua, C. (2020) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2020.107695 · Cited by 17
Abbasi, O., Dammers, J., Arrubla, J., Warbrick, T., Butz, M., Neuner, I., & Shah, N. J. (2015) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2015.07.011 · Cited by 15
Minn, S., & Côté, J. N. (2018) · Journal of Electromyography and Kinesiology · doi:10.1016/j.jelekin.2018.08.001 · Cited by 15
Dauwels, J., Vialatte, F., & Cichocki, A. S. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-69158-7_13 · Cited by 15
“In particular, a common hypothesis, proposed about three decades ago, is that basal forebrain neurons may be severely affected in AD and result in a cerebral cholinergic deficit, underlying memory loss and other cognitive symptoms (Jeong et al., 2001).”
Information-theoretical noninvasive damage detection in bridge structures
Ambegedara, A. S., Sun, J., Janoyan, K. D., & Bollt, E. M. (2016) · Chaos An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/1.4967920 · Cited by 14
Huang, S., Wang, T., Zhang, X., Yen, C., Liang, J., Zeng, L., & Luo, Y. (2018) · Respiratory Physiology & Neurobiology · doi:10.1016/j.resp.2018.06.006 · Cited by 14
Are there sex differences in muscle coordination of the upper girdle during a sustained motor task?
Cid, M. M., Oliveira, A. B., Januário, L. B., Côté, J. N., Moreira, R. D. F. C., & Madeleine, P. (2019) · Journal of Electromyography and Kinesiology · doi:10.1016/j.jelekin.2019.01.003 · Cited by 14
“mutual information detecting both linear and non-linear dependencies (Jeong et al., 2001) has been applied to sEMG signals”
BackgroundZhang, M., Zheng, C., Quan, M., An, L., Yang, Z., & Zhang, T. (2011) · Neurosignals · doi:10.1159/000329302 · Cited by 14
Decreased Lempel-Ziv complexity in Alzheimer's disease patients' magnetoencephalograms
Gómez, C., Hornero, R., Abásolo, D., López, M., & Fernández, A. (2005) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/iembs.2005.1615472 · Cited by 14
“Other studies found that the local cross-mutual information [13] and the Global Field Synchronization [14] can distinguish between control subjects and AD patients.”
BackgroundQuantitative sleep EEG synchronization analysis for automatic arousals detection
Erdamar, A., & Akşahin, M. F. (2020) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2020.101895 · Cited by 14
Linear and Nonlinear EEG Synchronization in Alzheimer’s Disease
Vyšata, O., Vališ, M., Procházka, A., Rusina, R., & Pazdera, L. (2015) · Neurophysiology · doi:10.1007/s11062-015-9496-z · Cited by 14
“A certain reduction in mutual information has been previously reported in the frontal and right anterior temporal areas and in the inter-hemispheric pathways in the respective cases [10].”
BackgroundAderinwale, A., Tolossa, G. B., Kim, A., Jang, E. H., Lee, Y.-I., Jeon, H. J., Kim, H., Yu, H. Y., & Jeong, J. (2023) · Psychiatry Research Neuroimaging · doi:10.1016/j.pscychresns.2023.111641 · Cited by 13
Self-citationMulti-scale entropy assessment of magnetoencephalography signals in schizophrenia
Wolfson, E. J., Fekete, T., Loewenstein, Y., & Shriki, O. (2024) · Scientific Reports · doi:10.1038/s41598-024-64704-2 · Cited by 14
Rosso, O. A., Mendes, A. S., Rostas, J. A. P., Hunter, M., & Moscato, P. (2008) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2008.10.017 · Cited by 13
“From the Information Theory point of view, the information contained in both channels is similar.”
BackgroundInfluentialCausality Analysis of fMRI Data Based on the Directed Information Theory Framework
Wang, Z., Alahmadi, A. H., Zhu, D. C., & Li, T. (2015) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2015.2481723 · Cited by 13
“For this reason, in recent years, there has been a growing interest in applying MI to neuroscience to measure the coupling strength among different brain regions or groups of neurons [38], [39].”
BackgroundZhao, Z., Cheng, Y., Li, Z., & Yu, Y. (2018) · Behavioural Neurology · doi:10.1155/2018/2191208 · Cited by 13
“Among the usually applied methods to study functional connectivity, such as coherence and correlation coefficient, mutual information (MI) has been widely applied in many studies to investigate the information communication and connectivity among different brain regions [11, 17, 18].”
MethodMutual information rate of nonstationary statistical signals
Miao, H., Zhang, F., & Tao, R. (2020) · Signal Processing · doi:10.1016/j.sigpro.2020.107531 · Cited by 13
Franciotti, R., Moretti, D. V., Benussi, A., Ferri, L., Russo, M., Carrarini, C., Barbone, F., Arnaldi, D., Falasca, N. W., Koch, G., et al. (2021) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2021.10.016 · Cited by 14
Miroshnichenko, G. G., Meigal, A. Y., Саенко, И. В., Gerasimova-Meigal, L. I., Черникова, Л. А., Субботина, Н. С., Rissanen, S. M., & Karjalainen, P. A. (2018) · Frontiers in Neuroscience · doi:10.3389/fnins.2018.00667 · Cited by 13
“MI decay time measures information transmission between a signal and its time shifted counterpart (Jeong et al., 2001) and, consequently, predictability of the signal (Ramanand et al.”
BackgroundThe effect of walking while typing on neck/shoulder patterns
Fedorowich, L. M., Emery, K., & Côté, J. N. (2015) · European Journal of Applied Physiology · doi:10.1007/s00421-015-3163-3 · Cited by 13
“Mutual information (MI) is a statistical method which accounts for both linear and non-linear relationships as it quantifies shared activation patterns of two muscles’ electromyographic time series (Jeong et al. 2001; Kojadinovic 2005).”
MethodAge‐ and disease‐related features of task‐related brain oscillations by using mutual information
Liu, C., Huang, C. H., Chou, C., Kuo, W., Lin, Y., Hung, C., Chen, T., & Ho, M. (2012) · Brain and Behavior · doi:10.1002/brb3.93 · Cited by 13
“Additionally, the CMI method quantifies information transmitted from one electrode to another, and it is good at assessing the strength of synchronization among areas that reflected precisely the coupling of these areas by corticocortical connections (Singer 1999; Jeong et al. 2001; Jin et al. 2010).”
BackgroundMethodInfluentialUsing EEG signals to assess workload during memory retrieval in a real-world scenario
Chiang, K.-J., Dong, S., Cheng, C.-K., & Jung, T. (2023) · Journal of Neural Engineering · doi:10.1088/1741-2552/accbed · Cited by 12
“MI within frontal channels, however, has been reported as a marker of Alzheimer’s and schizophrenic disease [32, 40].”
Ventouras, E.-C., Margariti, A., Chondraki, P., Kalatzis, I. K., Economou, N.-T., Tsekou, H., Paparrigopoulos, T. J., & Ktonas, P. Y. (2014) · Cognitive Neurodynamics · doi:10.1007/s11571-014-9319-8 · Cited by 12
Gómez, C., Poza, J., Gómez‐Pilar, J., Bachiller, A., Juan‐Cruz, C., Tola-Arribas, M. Á., Carreres, A., Cano, M., & Hornero, R. (2016) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2016.7591319 · Cited by 13
“[19] reported lower values of cross-mutual information in AD subjects than in controls, mainly over frontal and antero-temporal regions.”
BackgroundRewiring of neuronal networks during synaptic silencing
Dahlmanns, J. K., von Einem, V., Breininger, K., Dahlmanns, M., Maier, A., Kornhuber, J., & Groemer, T. W. (2017) · Scientific Reports · doi:10.1038/s41598-017-11729-5 · Cited by 12
Directional and Causal Information Flow in EEG for Assessing Perceived Audio Quality
Mehta, K., & Kliewer, J. (2017) · IEEE Transactions on Molecular Biological and Multi-Scale Communications · doi:10.1109/tmbmc.2018.2806454 · Cited by 14
“Similarly, other studies have used MI to analyze EEG data to investigate corticocortical information transmission for pathological conditions such as Alzheimer’s disease [15] and schizophrenia [16], or for odor stimulation [17], [18].”
Cantù, H., Nantel, J., Millán, M., Paquette, C., & Côté, J. N. (2019) · Frontiers in Neurology · doi:10.3389/fneur.2019.00951 · Cited by 12
“The MI outcome is a number that varies from 0 to 1, 0 indicating no connectivity and 1 complete connectivity within the muscle pair (34).”
Background
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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