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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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“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
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“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].”
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Dauwels, J., Vialatte, F., & Cichocki, A. S. (2010) · Current Alzheimer Research · doi:10.2174/156720510792231720 · Cited by 529
“The following measures have been applied in this context: approximate entropy [87, 88, 89, 90], sample entropy [87, 94], Tsallis entropy [82], multiscale entropy [87, 95], auto-mutual information [77, 87, 88], Lempel-Ziv complexity [87], universal compression [82], fractal dimension [91], correlation dimension [92, 93…”
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“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).”
Entropy analysis of the EEG background activity in Alzheimer's disease patients
Abásolo, D., Hornero, R., Espino, P., Álvarez, D., & Poza, J. (2006) · Physiological Measurement · doi:10.1088/0967-3334/27/3/003 · Cited by 347
“Keywords: Alzheimer’s disease, electroencephalogram, sample entropy, spectral entropy”
Benedetti, F., Arduino, C., Costa, S., Vighetti, S., Tarenzi, L., Rainero, I., & Asteggiano, G. (2006) · Pain · doi:10.1016/j.pain.2005.12.016 · Cited by 347
“As AD patients show decreased electroencephalogram (EEG) coherence (Jeong, 2004), we also analyzed the functional connectivity among different brain regions by means of EEG mutual information analysis (Jeong et al., 2001; Na et al., 2002; Min et al., 2003; David et al., 2004; Hurtado et al., 2004).”
MethodMagnetoencephalographic evaluation of resting-state functional connectivity in Alzheimer's disease
Stam, C. J., 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., Berendse, H. W., & Scheltens, P. (2006) · NeuroImage · doi:10.1016/j.neuroimage.2006.05.033 · Cited by 333
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Self-citationAbásolo, D., Hornero, R., Gómez, C., García, M., & Lopez, M. (2005) · Medical Engineering & Physics · doi:10.1016/j.medengphy.2005.07.004 · Cited by 314
“For instance, non-linear forecasting and entropy maps have been used to characterize drug effects on brain dynamics in AD [21] and mutual information analysis to assess information transmission between different cortical areas in AD [22].”
BackgroundAdvances in Quantitative Electroencephalogram Analysis Methods
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Analysis of electroencephalograms in Alzheimer's disease patients with multiscale entropy
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Supervised Network-Based Fuzzy Learning of EEG Signals for Alzheimer's Disease Identification
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“found that correlations of AD brain are lower between distant electrodes in the right hemisphere and between interhemispheric electrodes [15].”
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“This cortical disconnection is supported by a large number of electrophysiological studies demonstrating a loss of functional interactions using well-established linear (Jeong, 2004) and nonlinear coherence measures (Jeong et al., 2001; Stam et al., 2003).”
Abásolo, D., Hornero, R., Espino, P., Poza, J., Sánchez, C. I., & de la Rosa, R. (2005) · Clinical Neurophysiology · doi:10.1016/j.clinph.2005.04.001 · Cited by 247
“Most of these studies have been carried out applying the D 2 (Stam et al., 1995; Besthorn et al., 1995, 1997; Jeong et al., 1998; Jelles et al., 1999; Jeong et al., 2001a) or the first positive Lyapunov exponent (Jeong et al., 1998, 2001a).”
BackgroundResultInfluentialDisturbed fluctuations of resting state EEG synchronization in Alzheimer's disease
Stam, C. J., Montez, T., Jones, B. F., Rombouts, S. A. R. B., van der Made, Y., Pijnenburg, Y. A. L., & Scheltens, P. (2005) · Clinical Neurophysiology · doi:10.1016/j.clinph.2004.09.022 · Cited by 249
“Recently, these results have been confirmed with nonlinear measures of correlation between EEG and MEG signals such as the mutual information (Jeong et al., 2001) and the synchronization likelihood (Pijnenburg et al.”
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“Using this measure as the linear and non-linear statistical dependencies between two time series (cross-mutual information), indeed loss of functional connectivity has been demonstrated in patients with AD, in particular over the frontal and anterior-temporal regions (Jeong et al., 2001).”
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“For instance, research has focused on analyses of electroencephalography (EEG), magnetoencephalography (MEG), and functional MRI (fMRI) data (Jeong et al., 2001; Lizier et al., 2011; Vicente et al., 2011).”
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Age-related changes in the subcortical–cortical encoding and categorical perception of speech
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“, brainstem and cortical ERP), MI offers a means to detect both linear and nonlinear statistical dependencies between brain responses (correlations which measure only linear dependences) (Jeong et al., 2001).”
A spatio-temporal wavelet-chaos methodology for EEG-based diagnosis of Alzheimer's disease
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“All these techniques can be applied to relatively short, noisy and non-stationary time series, irrespective of whether their origin is stochastic or deterministic (Lempel & Ziv 1976; Richman & Moorman 2000; Jeong et al. 2001b; Pincus 2001; Costa et al. 2005).”
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Na, S. H., Jin, S., Kim, S. Y., & Ham, B. (2002) · Clinical Neurophysiology · doi:10.1016/s1388-2457(02)00197-9 · Cited by 163
“Because the decreasing slope of the AMI is positively correlated with entropy (Pompe, 1993; Palus, 1994, 1996b), we calculated the slope of the AMI profile and used it as a complexity measure of EEG.”
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Quantitative EEG in early Alzheimer's disease patients — Power spectrum and complexity features
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“The decrease of EEG complexity can also be due to the loss of neurons or synapses, since they are associated with the complex dynamical processing within the brain neural networks [33].”
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“Then, AMI curves show a transitory oscillation which decays as s increases [25].”
InfluentialOpoku Asare, K., Terhorst, Y., Vega, J., Peltonen, E., Lagerspetz, E., & Ferreira, D. (2021) · JMIR mHealth and uHealth · doi:10.2196/26540 · Cited by 145
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Alzheimer's Disease: Models of Computation and Analysis of EEGs
Adeli, H., Ghosh‐Dastidar, S., & Dadmehr, N. (2005) · Clinical EEG and Neuroscience · doi:10.1177/155005940503600303 · Cited by 141
Diagnosis of Alzheimer’s disease with Electroencephalography in a differential framework
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Feature-Based Time-Series Analysis
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MATLAB toolbox for functional connectivity
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EEG filtering based on blind source separation (BSS) for early detection of Alzheimer's disease
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Sample Entropy Tracks Changes in Electroencephalogram Power Spectrum With Sleep State and Aging
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BackgroundInfluentialA Longitudinal EEG Study of Alzheimer's Disease Progression Based on A Complex Network Approach
Morabito, F. C., Campolo, M., Labate, D., Morabito, G., Bonanno, L., Bramanti, A., Salvo, S. D., Marra, A., & Bramanti, P. (2014) · International Journal of Neural Systems · doi:10.1142/s0129065715500057 · Cited by 111
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“Kargieman et al. (2008) pointed out that noncompetitive N-methyl-D-aspartate receptor antagonists are utilized as pharmacological models of schizophrenia since they are capable to exacerbate schizophrenia symptoms.”
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EEG Patterns in Mild Cognitive Impairment (MCI) Patients
Baker, M. C., Akrofi, K., Schiffer, R. W., & ́Boyle, M. O. (2008) · The Open Neuroimaging Journal · doi:10.2174/1874440000802010052 · Cited by 99
“Still other indicators of EEG synchrony include synchronization likelihood (SL) [23], cross mutual information (CMI) and Auto MI (AMI) [24], as well as global field synchronization (GFS) [25].”
BackgroundFunctional networks of the brain: from connectivity restoration to dynamic integration
Hramov, A. E., Frolov, N. S., Maksimenko, V. A., Kurkin, S., Kazantsev, V., & Pisarchik, A. N. (2020) · Physics-Uspekhi · doi:10.3367/ufne.2020.06.038807 · Cited by 100
Automatic diagnosis of mild cognitive impairment using electroencephalogram spectral features
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Spectral and Nonlinear Analyses of MEG Background Activity in Patients With Alzheimer's Disease
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“Moreover, AD has also been studied by applying connectivity measures such as mutual information and synchronization likelihood to EEG/MEG data [19]–[21].”
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Complexity analysis of EEG in patients with schizophrenia using fractal dimension
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“…measures (Acharya et al 2005, Fell et al 2000b, Abasolo et al 2008), mutual information indices (Na et al 2002, Stam et al 1996, Abasolo et al 2008, Jeong et al 2001) and omega complexity (Kim et al 2001, Saito et al 1998, Kondakor et al 1997, Szelenberger et al 1996, Irisawa et al 2006) have been…”
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“FC is fundamentally a statistical concept, and is typically assessed using statistical measures such as correlation (Biswal et al., 1995), cross-coherence (Sun et al., 2004), and mutual information (Jeong et al., 2001).”
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“In order to designate the direction of information flow, time-delayed mutual information (TDMI) has been introduced and used to detect whether there are differences between control groups and groups of patients with various neurological disorders (Jeong et al., 2001; Na et al., 2002).”
InfluentialCharacterization of kinesthetic motor imagery compared with visual motor imageries
Yang, Y. J., Jeon, E. J., Kim, J. S., & Chung, C. K. (2021) · Scientific Reports · doi:10.1038/s41598-021-82241-0 · Cited by 82
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“Mutual information (MI), which can be defined as the amount of functional or shared connectivity between two muscles, is another statistical method of quantifying co-ordination patterns between muscles by accounting for both linear and non-linear relationships between two EMG time series (Jeong et al., 2001; Kojadinov…”
MethodEffects of rTMS on the brain: is there value in variability?
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“These changes are likely due to decreased non-linear cell-dynamics and/or nonlinear couplings among cortical areas as well as linear couplings [8], [9].”
BackgroundEEG network connectivity changes in mild cognitive impairment — Preliminary results
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“The observed loss of synchrony was interpreted as functional disconnection between different cortical regions which cannot simply be due to the loss of cortical neurons (Jeong et al., 2001; Schliebs and Arendt, 2011).”
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Functional Brain Imaging in Schizophrenia: Selected Results and Methods
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“1993), and have since expanded into fMRI, magnetoencephalography (MEG), electroencephalography (EEG), and peripheral physiological measures (Friston 1994; Sun et al. 2004; Salvador et al. 2005; David et al. 2004; Chen et al. 2008; Jeong et al. 2001).”
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“mation-based method that has been used to diagnose Alzheimer’s disease and schizophrenia [51–53].”
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“Reduction of CMI over frontal and anterotemporal regions and between interhemispheric electrodes of AD patients was observed as compared with normal controls (Jeong et al., 2001).”
ResultAmjad, I., Toor, H. G., Niazi, I. K., Afzal, H., Jochumsen, M., Shafique, M. S., Allen, K., Haavik, H., & Ahmed, T. (2018) · International Journal of Neuroscience · doi:10.1080/00207454.2018.1551894 · Cited by 76
“There are various measures one can use to determine the complexity of EEG, including sample entropy [49], ApEn [31], auto-mutual information [50], and Lempel – Zev complexity [51,52].”
MethodA mutual information analysis of neural coding of speech by low-frequency MEG phase information
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“i te fe j k j ts k fs i i i Y WF te ts fe fs = = = − − ∑ ∑ [7]”
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“The next step is to calculate the MI as defined by Jeong et al. (2001): MIXY ¼ X pxyðxi; yjÞ log pXYðxi; yjÞ pX xið ÞPYðyjÞ ; ð2Þ where pXY (xi,yj) is the joint probability density function for the EMG signals of X and Y.”
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“Mutual information analysis also showed that connections between the frontal and antero-temporal regions are weaker in AD patients [41].”
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Disturbed Beta Band Functional Connectivity in Patients With Mild Cognitive Impairment: An MEG Study
Gómez, C., Stam, C. J., Hornero, R., Fernández, A., & Maestú, F. (2009) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2009.2018454 · Cited by 67
“Recently, other synchronization methods have been used to analyze the EEG/MEG activity in AD, such as cross-mutual information [ 42 ], global field synchronization [14], and SL [8], [37].”
MethodMutual information measures applied to EEG signals for sleepiness characterization
Melia, U. S. P., Guaita, M., Vallverdú, M., Embid, C., Vilaseca, I., Salamero, M., & Santamaría, J. (2015) · Medical Engineering & Physics · doi:10.1016/j.medengphy.2015.01.002 · Cited by 64
Nonlinear dynamics of EEG in Alzheimer's disease
Jeong, J. (2002) · Drug Development Research · doi:10.1002/ddr.10061 · Cited by 64
“Recently, the mutual information (MI) method was applied to the EEG in AD patients [Jeong et al., 2001b].”
MethodResultSelf-citationComplexity Analysis of Spontaneous Brain Activity in Alzheimer Disease and Mild Cognitive Impairment
Fernández, A., Hornero, R., Gómez, C., Turrero, A., Gil‐Gregorio, P., Santos, J. M., & Ortiz, T. (2010) · Alzheimer Disease & Associated Disorders · doi:10.1097/wad.0b013e3181c727f7 · Cited by 62
EEG amplitude modulation analysis for semi-automated diagnosis of Alzheimer’s disease
Falk, T. H., Fraga, F. J., Trambaiolli, L. R., & Anghinah, R. (2012) · EURASIP Journal on Advances in Signal Processing · doi:10.1186/1687-6180-2012-192 · Cited by 61
“In general, studies have agreed that AD causes a decrease in EEG pattern complexity [20-26], a factor likely caused by the reduction in non-linear connections between cortical regions, neuronal death, or even deficiency of neurotransmitters [27].”
BackgroundAbnormal functional connectivity in focal hand dystonia: Mutual information analysis in EEG
Jin, S., Lin, P. T., Auh, S., & Hallett, M. (2011) · Movement Disorders · doi:10.1002/mds.23675 · Cited by 60
How reliable are the functional connectivity networks of MEG in resting states?
Jin, S., Seol, J., Kim, J. S., & Chung, C. K. (2011) · Journal of Neurophysiology · doi:10.1152/jn.00335.2011 · Cited by 59
Supervised dictionary learning of EEG signals for mild cognitive impairment diagnosis
Kashefpoor, M., Rabbani, H., & Barekatain, M. (2019) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2019.101559 · Cited by 57
Madeleine, P., Samani, A., Binderup, A. T., & Stensdotter, A. (2009) · Scandinavian Journal of Medicine and Science in Sports · doi:10.1111/j.1600-0838.2009.01037.x · Cited by 55
“Mutual information detects both linear and nonlinear dependencies (Jeong et al., 2001; Kojadinovic, 2005) and can be used as a measure of functional connectivity between sub-divisions in relation to high-intensity eccentric exercise.”
BackgroundMethodInfluentialWicks, R. T., Chapman, S. C., & Dendy, R. O. (2007) · Physical Review E · doi:10.1103/physreve.75.051125 · Cited by 54
“wind, and the ionospheric response detected by ground based measurements on Earth [10]; and within the brains of Alzheimer’s disease patients [11].”
BackgroundLee, S., Yoon, S., Kim, J.-I., Jin, S., & Chung, C. K. (2014) · Progress in Neuro-Psychopharmacology and Biological Psychiatry · doi:10.1016/j.pnpbp.2014.01.008 · Cited by 53
“FC networks have been studied with the aim of characterizing the differences betweennormal controls and patientswith brain network diseases such as Alzheimer's disease, using MEG (Montez et al., 2009; Stam et al., 2009) or EEG (Stam et al., 2007), and schizophrenia, using MEG (Bassett et al., 2009) or EEG (Rubinov et…”
MethodDimensional complexity of the EEG in patients with posttraumatic stress disorder
Chae, J., Jeong, J., Peterson, B. S., Kim, D., Bahk, W., Jun, T., Kim, S.-Y., & Kim, K. (2004) · Psychiatry Research Neuroimaging · doi:10.1016/j.pscychresns.2003.05.002 · Cited by 53
“…al., 1990; Lehnertz and Elger, 1998), Alzheimer’s disease (Pritchard et al., 1994; Besthorn et al., 1995; Stam et al., 1996; Jeong et al., 1998c, 2001b), Parkinson’s disease(Stam et al., 1994, 1995), schizophrenia (Jeong et al., 1998b; Kim et al., 2000), and sleep deprivation(Jeong et al., 2001a).”
MethodSelf-citationComplex network analysis of MCI-AD EEG signals under cognitive and resting state
Das, S. S., & Puthankattil, S. D. (2020) · Brain Research · doi:10.1016/j.brainres.2020.146743 · Cited by 52
Power shift and connectivity changes in healthy aging during resting-state EEG
Perinelli, A., Assecondi, S., Tagliabue, C. F., & Mazza, V. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119247 · Cited by 54
Effects of Mental Fatigue on Small-World Brain Functional Network Organization
Li, G., Luo, Y., Zhang, Z., Xu, Y., Jiao, W., Jiang, Y., Huang, S., & Wang, C. (2019) · Neural Plasticity · doi:10.1155/2019/1716074 · Cited by 50
Robust EEG Based Biomarkers to Detect Alzheimer’s Disease
Al-Nuaimi, A. H. H., Blūma, M., Al-Juboori, S., Eke, C. S., Jammeh, E., Sun, L., & Ifeachor, E. (2021) · Brain Sciences · doi:10.3390/brainsci11081026 · Cited by 49
“The 12 EEG channels selected have been reported in other studies as having a signifi-cant role in AD detection [101,147–150].”
Gómez, C., Hornero, R., Abásolo, D., Fernández, A., & Escudero, J. (2007) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2007.07.001 · Cited by 49
“[19] have used automutual information (AMI) and CMI to classify AD patients and control subjects’ EEGs.”
BackgroundMethodInfluentialAlonso, J. F., Poza, J., Mañanas, M. Á., Romero, S., Fernández, A., & Hornero, R. (2010) · Annals of Biomedical Engineering · doi:10.1007/s10439-010-0155-7 · Cited by 49
Monllor, P., Cervera‐Ferri, A., Lloret, M.-Á., Esteve, D., Lopez, B., Leon, J.-L., & Lloret, A. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms221910889 · Cited by 49
Sun, C., Yang, F., Wang, C., Wang, Z., Zhang, Y., Ming, D., & Du, J. (2018) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2018.00285 · Cited by 47
“Because neural dynamics almost certainly includes many highly nonlinear processes, MI analysis may be helpful in understanding and quantifying the nonlinear transmission of information within the brain (Jeong et al., 2001).”
BackgroundMethodValizadeh, S. A., Riener, R., Elmer, S., & Jäncke, L. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.04.005 · Cited by 50
Hyperconnectivity in Dementia Is Early and Focal and Wanes with Progression
Bonanni, L., Moretti, D. V., Benussi, A., Ferri, L., Russo, M., Carrarini, C., Barbone, F., Arnaldi, D., Falasca, N. W., Koch, G., et al. (2020) · Cerebral Cortex · doi:10.1093/cercor/bhaa209 · Cited by 47
Paitel, E. R., Otteman, C., Polking, M. C., Licht, H. J., & Nielson, K. A. (2025) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2025.1496235 · Cited by 53
Escudero, J., Hornero, R., & Abásolo, D. (2009) · Physiological Measurement · doi:10.1088/0967-3334/30/2/006 · Cited by 46
“For example, the AMIFRD of electroencephalogram (EEG) and magnetoencephalogram (MEG) recordings characterized patients with Alzheimer’s disease in contrast to control subjects (Abásolo et al 2008, Jeong et al 2001, Gómez et al 2007).”
BackgroundMethodResultInfluentialJin, S., Kwon, Y., Jeong, J.-S., Kwon, S.-W., & Shin, D.-H. (2006) · Brain and Cognition · doi:10.1016/j.bandc.2006.05.001 · Cited by 46
Self-citationMuscle coordination and force variability during static and dynamic tracking tasks
Svendsen, J. H., Samani, A., Mayntzhusen, K., & Madeleine, P. (2011) · Human Movement Science · doi:10.1016/j.humov.2011.02.001 · Cited by 45
Planetary-Gearbox Fault Classification by Convolutional Neural Network and Recurrence Plot
Wang, D.-F., Guo, Y., Wu, X., Na, J., & Litak, G. (2020) · Applied Sciences · doi:10.3390/app10030932 · Cited by 45
“Mutual information I(Q, S) could be computed as in [17] by:”
BackgroundMethodLu, C., Teng, S., Hung, C., Tseng, P.-J., Lin, L.-T., Lee, P., & Wu, Y. (2011) · Clinical Neurophysiology · doi:10.1016/j.clinph.2011.01.050 · Cited by 44
Mutual‐information‐based approach for neural connectivity during self‐paced finger lifting task
Chen, C., Hsieh, J., Wu, Y., Lee, P., Chen, S., Niddam, D. M., Yeh, T., & Wu, Y. (2007) · Human Brain Mapping · doi:10.1002/hbm.20386 · Cited by 44
“The cross mutual information (CMI) method in a time-domain has been developed to quantify and assess the functional impairment of information transmission from one area to another in Alzheimer patients [Jeong et al., 2001].”
MethodDrug effect on EEG connectivity assessed by linear and nonlinear couplings
Alonso, J. F., Mañanas, M. Á., Romero, S., Hoyer, D., Jordi Riba, A., & Barbanoj, M. J. (2009) · Human Brain Mapping · doi:10.1002/hbm.20881 · Cited by 44
“…has been overcome by information transfer functions such as CMIF, which has been confirmed as an appropriate approach for the analysis of time scale related complexity between biomedical signals series [Chun-Chuan et al., 2008; Jeong et al., 2001; Jin et al., 2006a,b; Na et al., 2002, 2006].”
BackgroundMethodInfluentialCombining Classification with fMRI-Derived Complex Network Measures for Potential Neurodiagnostics
Fekete, T., Wilf, M., Rubin, D., Edelman, S., Malach, R., & Mujica-Parodi, L. R. (2013) · PLoS ONE · doi:10.1371/journal.pone.0062867 · Cited by 43
Youssef, N., Xiao, S., Liu, M., Lian, H., Li, R., Chen, X., Zhang, W., Zheng, X., Li, Y., & Li, Y. (2021) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2021.698386 · Cited by 44
“Specifically, Jeong et al. (2001) study observed an overall decrease in connectivity values within frontal and anterior-temporal regions.”
BackgroundEpoch-based Entropy for Early Screening of Alzheimer’s Disease
Houmani, N., Dreyfus, G., & Vialatte, F. (2015) · International Journal of Neural Systems · doi:10.1142/s012906571550032x · Cited by 39
Min, B.-C. (2003) · Chemical Senses · doi:10.1093/chemse/bjg066 · Cited by 37
“We computed the time-delayed mutual information as follows (Jeong et al., 2001; Na et al., 2002):”
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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