Electroencephalographic Cross-Frequency Coupling as a Sign of Disease Progression in Patients With Mild Cognitive Impairment: A Pilot Study.
paper
Cited
Public
- Authors
- Musaeus, Christian Sandøe; Nielsen, Malene Schjønning; Musaeus, Jørgen Sandøe; Høgh, Peter
- Year
- 2020
- Journal
- Frontiers in neuroscience
- PMID
- 32848563
- DOI
- 10.3389/fnins.2020.00790
- PMCID
- PMC7431634
Mild cognitive impairment (MCI) refers to mild objective cognitive deficits and is associated with the later development of Alzheimer's disease (AD). However, not all patients with MCI convert to AD. EEG spectral power has shown promise as a marker of progression, but brain oscillations in different frequencies are not isolated entities. Coupling between different frequency bands, so-called cross-frequency coupling (CFC), has been associated with memory function and may further contribute to our understanding of what characterizes patients with MCI who progress to AD. In the current study, we wanted to investigate the changes in gamma/theta CFC in patients with AD and MCI compared to HC and in patients with pMCI compared to patients with sMCI. Furthermore, we wanted to investigate the association with cognitive test scores. EEGs were included at baseline for 15 patients with AD, 25 patients with MCI, and 36 older HC, and the participants were followed for up to 3 years. To investigate CFC, we calculated the modulation index (MI), which has been shown to be less affected by noisy data compared to other techniques. We found that patients with pMCI showed a significantly lower global gamma/theta CFC compared to patients with sMCI. In addition, global gamma/theta CFC was significantly correlated with Addenbrooke's Cognitive Examination (ACE) score (-value = 0.030, rho = 0.527). Although not significant, patients with AD and MCI showed a lower gamma/theta CFC compared to HC. These findings suggest that gamma/theta CFC is important for proper cognitive functioning and that a decrease in gamma/theta CFC in patients with MCI may be a sign of progression. Gamma/theta CFC may therefore serve as a progression marker in MCI, but larger studies are needed to validate these findings.
Bar graph showing the gamma/theta cross-frequency coupling for progressed mild cognitive impairment (pMCI) or stable mild cognitive impairment (sMCI). The star indicates a significant difference between pMCI and sMCI (p-value = 0.004, t-value = –11.09).
Scatterplots showing the significant correlation between gamma/theta cross-frequency coupling and Addenbrooke’s Cognitive Examination for patients with mild cognitive impairment.
Bar graph showing the gamma/theta cross-frequency coupling for healthy controls and for mild cognitive impairment and Alzheimer’s disease patients.
Bar graph showing the global scores for healthy controls and for mild cognitive impairment and Alzheimer’s disease patients for the exploratory cross-frequency coupling analyses.
No entities extracted from this document yet.
No uploaded files.
Not in any collection.
| Citation | PMID | DOI | Status |
|---|---|---|---|
| AxmacherN.HenselerM. M.JensenO.WeinreichI.ElgerC. E.FellJ. (2010). Cross-frequency coupling supports multi-item working memory in the human hippocampus. Proc. Natl. Acad. Sci. U.S.A. 107 3228–3233. 10.1073/pnas.0911531107 20133762PMC2840289 | — | — | — |
| BazzigaluppiP.BeckettT. L.KoletarM. M.LaiA. Y.JooI. L.BrownM. E. (2018). Early-stage attenuation of phase-amplitude coupling in the hippocampus and medial prefrontal cortex in a transgenic rat model of Alzheimer’s disease. J. Neurochem. 144 669–679. 10.1111/jnc.14136 28777881 | — | — | — |
| BuzsákiG.WatsonB. O. (2012). Brain rhythms and neural syntax: implications for efficient coding of cognitive content and neuropsychiatric disease. Dialogues Clin. Neurosci. 14 345–367.2339341310.31887/DCNS.2012.14.4/gbuzsakiPMC3553572 | — | — | — |
| CanoltyR. T.KnightR. T. (2010). The functional role of cross-frequency coupling. Trends Cogn. Sci. 14 506–515. 10.1016/j.tics.2010.09.001 20932795PMC3359652 | — | — | — |
| CobenL. A.DanzigerW.StorandtM. (1985). A longitudinal EEG study of mild senile dementia of Alzheimer type: changes at 1 year and at 2.5 years. Electroencephal. Clin. Neurophysiol. 61 101–112. 10.1016/0013-4694(85)91048-x2410219 | — | — | — |
| DelormeA.MakeigS. (2004). EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J. Neurosci. Methods 134 9–21. 10.1016/j.jneumeth.2003.10.009 15102499 | — | — | — |
| DimitriadisS. I.LaskarisN. A.BitzidouM. P.TarnanasI.TsolakiM. N. (2015). A novel biomarker of amnestic MCI based on dynamic cross-frequency coupling patterns during cognitive brain responses. Front. Neurosci. 9:350. 10.3389/fnins.2015.00350 26539070PMC4611062 | — | — | — |
| DinizB. S.Pinto JuniorJ. A.ForlenzaO. V. (2008). Do CSF total tau, phosphorylated tau, and beta-amyloid 42 help to predict progression of mild cognitive impairment to Alzheimer’s disease? A systematic review and meta-analysis of the literature. World J. Biol. Psychiatry 9 172–182. 10.1080/15622970701535502 17886169 | — | — | — |
| EngedalK.SnaedalJ.HoeghP.JelicV.Bo AndersenB.NaikM. (2015). Quantitative EEG applying the statistical recognition pattern method: a useful tool in dementia diagnostic workup. Dement. Geriatr. Cogn. Disord. 40 1–12. 10.1159/000381016 25895831 | — | — | — |
| GaltonC. J.ErzincliogluS.SahakianB. J.AntounN.HodgesJ. R. (2005). A comparison of the Addenbrooke’s Cognitive Examination (ACE), conventional neuropsychological assessment, and simple MRI-based medial temporal lobe evaluation in the early diagnosis of Alzheimer’s disease. Cogn. Behav. Neurol. 18 144–150. 10.1097/01.wnn.0000182831.47073.e916175017 | — | — | — |
| GoodmanM. S.KumarS.ZomorrodiR.GhazalaZ.CheamA. S. M.BarrM. S. (2018). Theta-gamma coupling and working memory in Alzheimer’s dementia and mild cognitive impairment. Front. Aging Neurosci. 10:101. 10.3389/fnagi.2018.00101 29713274PMC5911490 | — | — | — |
| GoutagnyR.GuN.CavanaghC.JacksonJ.ChabotJ.-G.QuirionR. (2013). Alterations in hippocampal network oscillations and theta-gamma coupling arise before Aβ overproduction in a mouse model of Alzheimer’s disease. Eur. J. Neurosci. 37 1896–1902. 10.1111/ejn.12233 23773058 | — | — | — |
| GrunerW. (2020). MANCOVAN MATLAB Central File Exchange. Avaliable at: https://www.mathworks.com/matlabcentral/fileexchange/27014-mancovan. (accessed July 17, 2020). | — | — | — |
| HülsemannM. J.NaumannE.RaschB. (2019). Quantification of phase-amplitude coupling in neuronal oscillations: comparison of phase-locking value, mean vector length, modulation index, and generalized-linear-modeling-cross-frequency-coupling. Front. Neurosci. 13:573. 10.3389/fnins.2019.00573 31275096PMC6592221 | — | — | — |
| JackC. R.Jr.HoltzmanD. M. (2013). Biomarker modeling of Alzheimer’s disease. Neuron 80 1347–1358.2436054010.1016/j.neuron.2013.12.003PMC3928967 | — | — | — |
| JelicV.JohanssonS. E.AlmkvistO.ShigetaM.JulinP.NordbergA. (2000). Quantitative electroencephalography in mild cognitive impairment: longitudinal changes and possible prediction of Alzheimer’s disease. Neurobiol. Aging 21 533–540. 10.1016/s0197-4580(00)00153-610924766 | — | — | — |
| JeongJ. (2004). EEG dynamics in patients with Alzheimer’s disease. Clin. Neurophysiol. 115 1490–1505.1520305010.1016/j.clinph.2004.01.001 | — | — | — |
| LeeT. W.GirolamiM.SejnowskiT. J. (1999). Independent component analysis using an extended infomax algorithm for mixed subgaussian and supergaussian sources. Neural. Comput. 11 417–441. 10.1162/089976699300016719 9950738 | — | — | — |
| LismanJ. E.JensenO. (2013). The θ-γ neural code. Neuron 77 1002–1016.2352203810.1016/j.neuron.2013.03.007PMC3648857 | — | — | — |
| MaioliF.CoveriM.PagniP.ChiandettiC.MarchettiC.CiarrocchiR. (2007). Conversion of mild cognitive impairment to dementia in elderly subjects: a preliminary study in a memory and cognitive disorder unit. Arch. Gerontol. Geriatr. 44 (Suppl. 1), 233–241. 10.1016/j.archger.2007.01.032 17317458 | — | — | — |
| MathuranathP. S.NestorP. J.BerriosG. E.RakowiczW.HodgesJ. R. (2000). A brief cognitive test battery to differentiate Alzheimer’s disease and frontotemporal dementia. Neurology 55 1613–1620. 10.1212/01.wnl.0000434309.85312.1911113213 | — | — | — |
| McKhannG. M.KnopmanD. S.ChertkowH.HymanB. T.JackC. R.Jr.KawasC. H. (2011). The diagnosis of dementia due to Alzheimer’s disease: recommendations from the national institute on aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimer’s Dement. 7 263–269.2151425010.1016/j.jalz.2011.03.005PMC3312024 | — | — | — |
| MiskovicV.AshbaughA. R.SantessoD. L.McCabeR. E.AntonyM. M.SchmidtL. A. (2010). Frontal brain oscillations and social anxiety: a cross-frequency spectral analysis during baseline and speech anticipation. Biol. Psychol. 83 125–132. 10.1016/j.biopsycho.2009.11.010 19945500 | — | — | — |
| MitchellJ.ArnoldR.DawsonK.NestorP. J.HodgesJ. R. (2009). Outcome in subgroups of mild cognitive impairment (MCI) is highly predictable using a simple algorithm. J. Neurol. 256 1500–1509. 10.1007/s00415-009-5152-0 19434441 | — | — | — |
| MusaeusC. S.EngedalK.HoghP.JelicV.MorupM.NaikM. (2018a). EEG theta power is an early marker of cognitive decline in dementia due to Alzheimer’s Disease. J. Alzheimer’s Dis. 64 1359–1371. 10.3233/jad-180300 29991135 | — | — | — |
| MusaeusC. S.NielsenM. S.HoghP. (2019a). Altered low-frequency EEG connectivity in mild cognitive impairment as a sign of clinical progression. J. Alzheimer’s Dis. 68 947–960. 10.3233/jad-181081 30883355 | — | — | — |
| MusaeusC. S.NielsenM. S.HøghP. (2019b). Microstates as disease and progression markers in patients with mild cognitive impairment. Front. Neurosci. 13:563. 10.3389/fnins.2019.00563 31263397PMC6584800 | — | — | — |
| MusaeusC. S.NielsenM. S.OsterbyeN. N.HoghP. (2018b). Decreased parietal beta power as a sign of disease progression in patients with mild cognitive impairment. J. Alzheimer’s Dis. 65 475–487. 10.3233/jad-180384 30056426 | — | — | — |
| OostenveldR.FriesP.MarisE.SchoffelenJ. M. (2011). FieldTrip: open source software for advanced analysis of MEG, EEG, and invasive electrophysiological data. Comput. Intell. Neurosci. 2011:156869.10.1155/2011/156869PMC302184021253357 | — | — | — |
| PalvaS.PalvaJ. M. (2007). New vistas for alpha-frequency band oscillations. Trends Neurosci. 30 150–158. 10.1016/j.tins.2007.02.001 17307258 | — | — | — |
| PetersenR. C. (2004). Mild cognitive impairment as a diagnostic entity. J. Int. Med. 256 183–194. 10.1111/j.1365-2796.2004.01388.x 15324362 | — | — | — |
| PetersenR. C.SmithG. E.WaringS. C.IvnikR. J.TangalosE. G.KokmenE. (1999). Mild cognitive impairment: clinical characterization and outcome. Arch. Neurol. 56 303–308.1019082010.1001/archneur.56.3.303 | — | — | — |
| PoilS. S.de HaanW.van der FlierW. M.MansvelderH. D.ScheltensP.Linkenkaer-HansenK. (2013). Integrative EEG biomarkers predict progression to Alzheimer’s disease at the MCI stage. Front. Aging Neurosci. 5:58. 10.3389/fnagi.2013.00058 24106478PMC3789214 | — | — | — |
| PozaJ.BachillerA.GomezC.GarciaM.NunezP.Gomez-PilarJ. (2017). Phase-amplitude coupling analysis of spontaneous EEG activity in Alzheimer’s disease. Conf. Proc. 2017 2259–2262.10.1109/EMBC.2017.803730529060347 | — | — | — |
| RozziniL.Vicini ChiloviB.BertolettiE.ContiM.DelrioI.TrabucchiM. (2008). The importance of Alzheimer disease assessment scale-cognitive part in predicting progress for amnestic mild cognitive impairment to Alzheimer disease. J. Geriatr. Psychiatry Neurol. 21 261–267. 10.1177/0891988708324940 19017783 | — | — | — |
| SaxtonJ.SnitzB. E.LopezO. L.IvesD. G.DunnL. O.FitzpatrickA. (2009). Functional and cognitive criteria produce different rates of mild cognitive impairment and conversion to dementia. J. Neurol. Neurosurg. Psychiatry 80 737–743. 10.1136/jnnp.2008.160705 19279031PMC2698042 | — | — | — |
| Schjonning NielsenM.SimonsenA. H.SiersmaV.EngedalK.JelicV.AndersenB. B. (2018). Quantitative electroencephalography analyzed by statistical pattern recognition as a diagnostic and prognostic tool in mild cognitive impairment: results from a nordic multicenter cohort study. Dement. Geriatr. Cogn. Disord. Extra 8 426–438. 10.1159/000490788 30631335PMC6323395 | — | — | — |
| TortA. B. L.KomorowskiR. W.MannsJ. R.KopellN. J.EichenbaumH. (2009). Theta-gamma coupling increases during the learning of item-context associations. Proc. Natl. Acad. Sci. U.S.A. 106 20942–20947. 10.1073/pnas.0911331106 19934062PMC2791641 | — | — | — |
| TortA. B. L.KramerM. A.ThornC.GibsonD. J.KubotaY.GraybielA. M. (2008). Dynamic cross-frequency couplings of local field potential oscillations in rat striatum and hippocampus during performance of a T-maze task. Proc. Natl. Acad. Sci. U.S.A. 105 20517–20522. 10.1073/pnas.0810524105 19074268PMC2629291 | — | — | — |
| WangJ.FangY.WangX.YangH.YuX.WangH. (2017). Enhanced Gamma activity and cross-frequency interaction of resting-state electroencephalographic oscillations in patients with Alzheimer’s Disease. Front. Aging Neurosci. 9:243. 10.3389/fnagi.2017.00243 28798683PMC5526997 | — | — | — |
| WhithamE. M.LewisT.PopeK. J.FitzgibbonS. P.ClarkC. R.LovelessS. (2008). Thinking activates EMG in scalp electrical recordings. Clin. Neurophysiol. 119 1166–1175. 10.1016/j.clinph.2008.01.024 18329954 | — | — | — |
| WhithamE. M.PopeK. J.FitzgibbonS. P.LewisT.ClarkC. R.LovelessS. (2007). Scalp electrical recording during paralysis: quantitative evidence that EEG frequencies above 20 Hz are contaminated by EMG. Clin. Neurophysiol. 118 1877–1888. 10.1016/j.clinph.2007.04.027 17574912 | — | — | — |
| WinbladB.PalmerK.KivipeltoM.JelicV.FratiglioniL.WahlundL. O. (2004). Mild cognitive impairment–beyond controversies, towards a consensus: report of the International Working Group on Mild Cognitive Impairment. J. Int. Med. 256 240–246.10.1111/j.1365-2796.2004.01380.x15324367 | — | — | — |
| ZhangX.ZhongW.BrankackJ.WeyerS. W.MullerU. C.TortA. B. (2016). Impaired theta-gamma coupling in APP-deficient mice. Sci. Rep. 6:21948.10.1038/srep21948PMC476493926905287 | — | — | — |
In this knowledge base
| Title | Year | PMID |
|---|---|---|
| Alcohol use disorder is associated with altered frontomedial phase-amplitude coupling strength during resting state. | 2026 | 41657495 |
External
| Title | Authors | Journal | Year | Link |
|---|---|---|---|---|
| Alcohol use disorder is associated with altered frontomedial phase-amplitude coupling strength during resting state. | Richard CD et al. | — | 2026 | → |
| Wearable EEG devices in the detection of mild cognitive impairment: a systematic review. | He C et al. | — | 2026 | → |
| Brain functional differences among ADHD subtypes in children revealed by phase-amplitude coupling analysis of resting-state EEG. | Tang W et al. | — | 2025 | → |
| Cell-type-specific contributions to theta-gamma coupled rhythms in the hippocampus. | Sengupta S et al. | — | 2025 | → |
| Electrical brain networks before and after transcranial pulsed shockwave stimulation in Alzheimer's patients. | Wojtecki L et al. | — | 2025 | → |
| Encephalography cross-frequency coupling and brain alteration in amyotrophic lateral sclerosis. | Benetton C et al. | — | 2025 | → |
| Exploring the diagnostic potential of EEG theta power and interhemispheric correlation of temporal lobe activities in Alzheimer's Disease through random forest analysis. | Lee AL et al. | — | 2025 | → |
| Exploring the neuromagnetic signatures of cognitive decline from mild cognitive impairment to Alzheimer's disease dementia. | Gaubert S et al. | — | 2025 | → |
| Functional and effective EEG connectivity patterns in Alzheimer's disease and mild cognitive impairment: a systematic review. | Paitel ER et al. | — | 2025 | → |
| Measuring mild cognitive impairment whole-brain electroencephalography phase-amplitude coupling connectivity using polar mutual information. | Zhang H et al. | — | 2025 | → |
| Measuring phase-amplitude coupling using dispersion fuzzy mutual information. | Zhang H et al. | — | 2025 | → |
| Multivariate empirical mode decomposition reveals markers of Alzheimer's Disease in the oscillatory response to transcranial magnetic stimulation. | Bernardi D et al. | — | 2025 | → |
| Reduced Functional Connectivity in the Default Mode Network in EEGs without Other Abnormalities in Early Creutzfeldt-Jacob Disease. | Pirker-Kees A et al. | — | 2025 | → |
| Reduced GABA<sub>A, slow</sub> synaptic inhibition in Alzheimer's disease. | MacIver MB et al. | — | 2025 | → |
| γ neuromodulations: unraveling biomarkers for neurological and psychiatric disorders. | Dai ZP et al. | — | 2025 | → |
| Alzheimer's disease diagnosis using rhythmic power changes and phase differences: a low-density EEG study. | Wang J et al. | — | 2024 | → |
| A qualitative exploration of 40 Hz sound and music for older adults with mild cognitive impairment. | Wang C et al. | — | 2024 | → |
| Cognitive and Neuropathophysiological Outcomes of Gamma-tACS in Dementia: A Systematic Review. | Manippa V et al. | — | 2024 | → |
| Cortical hyperexcitability in mouse models and patients with amyotrophic lateral sclerosis is linked to noradrenaline deficiency. | Scekic-Zahirovic J et al. | — | 2024 | → |
| Enhancing Motor Sequence Learning via Transcutaneous Auricular Vagus Nerve Stimulation (taVNS): An EEG Study. | Chen L et al. | — | 2024 | → |
| Functionally annotated electrophysiological neuromarkers of healthy ageing and memory function. | Auer T et al. | — | 2024 | → |
| How to design optimal brain stimulation to modulate phase-amplitude coupling? | Duchet B et al. | — | 2024 | → |
| Phase-Dependent Stimulation of the Hippocampus: A Computational Modeling Approach | Lee H et al. | — | 2024 | — |
| Advances in the study of cholinergic circuits in the central nervous system. | He G et al. | — | 2023 | → |
| Cross-Frequency Coupling and Intelligent Neuromodulation. | Yeh CH et al. | — | 2023 | → |
| Multiple cross-frequency coupling analysis of resting-state EEG in patients with mild cognitive impairment and Alzheimer's disease. | Chen X et al. | — | 2023 | → |
| Neuroprosthetics: from sensorimotor to cognitive disorders. | Gupta A et al. | — | 2023 | → |
| Subjective cognitive decline exhibits alterations of resting-state phase-amplitude coupling in precuneus. | Cheng CH et al. | — | 2023 | → |
| Toward the Identification of Neurophysiological Biomarkers for Alzheimer's Disease in Down Syndrome: A Potential Role for Cross-Frequency Phase-Amplitude Coupling Analysis. | Victorino DB et al. | — | 2023 | → |
| An update on the use of gamma (multi)sensory stimulation for Alzheimer's disease treatment. | Manippa V et al. | — | 2022 | → |
| Cholinergic Regulation of Hippocampal Theta Rhythm. | Gu Z et al. | — | 2022 | → |
| Cross-frequency coupling in psychiatric disorders: A systematic review. | Yakubov B et al. | — | 2022 | → |
| Sharpening Working Memory With Real-Time Electrophysiological Brain Signals: Which Neurofeedback Paradigms Work? | Jiang Y et al. | — | 2022 | → |
| Theta and gamma hippocampal-neocortical oscillations during the episodic-like memory test: Impairment in epileptogenic rats. | Malkov A et al. | — | 2022 | → |
| Toward noninvasive brain stimulation 2.0 in Alzheimer's disease. | Menardi A et al. | — | 2022 | → |
| The Theta Rhythm of the Hippocampus: From Neuronal and Circuit Mechanisms to Behavior. | Nuñez A et al. | — | 2021 | → |