Conversion from mild cognitive impairment to Alzheimer's disease is predicted by sources and coherence of brain electroencephalography rhythms

P. M. Rossini; C. Del Percio; P. Pasqualetti; E. Cassetta; G. Binetti; G. Dal Forno; F. Ferreri; G. Frisoni; P. Chiovenda; C. Miniussi; L. Parisi; M. Tombini; F. Vecchio; C. Babiloni

doi:10.1016/j.neuroscience.2006.08.049

Conversion from mild cognitive impairment to Alzheimer's disease is predicted by sources and coherence of brain electroencephalography rhythms

P. M. Rossini, C. Del Percio, P. Pasqualetti, E. Cassetta, G. Binetti, G. Dal Forno, F. Ferreri, G. Frisoni, P. Chiovenda, C. Miniussi, L. Parisi, M. Tombini, F. Vecchio, C. Babiloni

Research output: Contribution to journal › Article › peer-review

Abstract

Objective. Can quantitative electroencephalography (EEG) predict the conversion from mild cognitive impairment (MCI) to Alzheimer's disease (AD)? Methods. Sixty-nine subjects fulfilling criteria for MCI were enrolled; cortical connectivity (spectral coherence) and (low resolution brain electromagnetic tomography) sources of EEG rhythms (δ=2-4 Hz; θ=4-8 Hz; α 1=8-10.5 Hz; α 2=10.5-13 Hz: β 1=13-20 Hz; β 2=20-30 Hz; and γ=30-40) were evaluated at baseline (time of MCI diagnosis) and follow up (about 14 months later). At follow-up, 45 subjects were still MCI (MCI Stable) and 24 subjects were converted to AD (MCI Converted). Results. At baseline, fronto-parietal midline coherence as well as δ (temporal), θ (parietal, occipital and temporal), and α 1 (central, parietal, occipital, temporal, limbic) sources were stronger in MCI Converted than stable subjects (P

Original language	English
Pages (from-to)	793-803
Number of pages	11
Journal	Neuroscience
Volume	143
Issue number	3
DOIs	https://doi.org/10.1016/j.neuroscience.2006.08.049
Publication status	Published - Dec 13 2006

Keywords

Alzheimer's disease
cognitive decline
dementia
EEG
mild cognitive impairment
neurodegeneration

ASJC Scopus subject areas

Neuroscience(all)

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10.1016/j.neuroscience.2006.08.049

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Rossini, P. M., Del Percio, C., Pasqualetti, P., Cassetta, E., Binetti, G., Dal Forno, G., Ferreri, F., Frisoni, G., Chiovenda, P., Miniussi, C., Parisi, L., Tombini, M., Vecchio, F., & Babiloni, C. (2006). Conversion from mild cognitive impairment to Alzheimer's disease is predicted by sources and coherence of brain electroencephalography rhythms. Neuroscience, 143(3), 793-803. https://doi.org/10.1016/j.neuroscience.2006.08.049

Conversion from mild cognitive impairment to Alzheimer's disease is predicted by sources and coherence of brain electroencephalography rhythms. / Rossini, P. M.; Del Percio, C.; Pasqualetti, P.; Cassetta, E.; Binetti, G.; Dal Forno, G.; Ferreri, F.; Frisoni, G.; Chiovenda, P.; Miniussi, C.; Parisi, L.; Tombini, M.; Vecchio, F.; Babiloni, C.

In: Neuroscience, Vol. 143, No. 3, 13.12.2006, p. 793-803.

Research output: Contribution to journal › Article › peer-review

Rossini, PM, Del Percio, C, Pasqualetti, P, Cassetta, E, Binetti, G, Dal Forno, G, Ferreri, F, Frisoni, G, Chiovenda, P, Miniussi, C, Parisi, L, Tombini, M, Vecchio, F & Babiloni, C 2006, 'Conversion from mild cognitive impairment to Alzheimer's disease is predicted by sources and coherence of brain electroencephalography rhythms', Neuroscience, vol. 143, no. 3, pp. 793-803. https://doi.org/10.1016/j.neuroscience.2006.08.049

Rossini, P. M. ; Del Percio, C. ; Pasqualetti, P. ; Cassetta, E. ; Binetti, G. ; Dal Forno, G. ; Ferreri, F. ; Frisoni, G. ; Chiovenda, P. ; Miniussi, C. ; Parisi, L. ; Tombini, M. ; Vecchio, F. ; Babiloni, C. / Conversion from mild cognitive impairment to Alzheimer's disease is predicted by sources and coherence of brain electroencephalography rhythms. In: Neuroscience. 2006 ; Vol. 143, No. 3. pp. 793-803.

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AU - Rossini, P. M.

AU - Del Percio, C.

AU - Pasqualetti, P.

AU - Cassetta, E.

AU - Binetti, G.

AU - Dal Forno, G.

AU - Ferreri, F.

AU - Frisoni, G.

AU - Chiovenda, P.

AU - Miniussi, C.

AU - Parisi, L.

AU - Tombini, M.

AU - Vecchio, F.

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Conversion from mild cognitive impairment to Alzheimer's disease is predicted by sources and coherence of brain electroencephalography rhythms

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