OBJECTIVE: To establish a model for better identification of patients in very early stages of Alzheimer's disease, AD (including patients with amnestic MCI) using high-resolution EEG and genetic data. METHODS: A total of 26 patients in early stages of probable AD and 12 patients with amnestic MCI were included. Both groups were similar in age and education. All patients had a comprehensive neuropsychological examination and a high resolution EEG. Relative band power characteristics were calculated in source space (LORETA inverse solution for spectral data) and compared between groups. A logistic regression model was calculated including relative band-power at the most significant location, ApoE status, age, education and gender. RESULTS: Differences in the delta band at 34 temporo-posterior source locations (p<.01) between AD and MCI groups were detected after correction for multiple comparisons. Classification slightly increased when ApoE status was added (p=.06 maximum likelihood test). Adjustment of analyses for the confounding factors age, gender and education did not alter results. CONCLUSIONS: Quantitative EEG (qEEG) separates between patients with amnestic MCI and patients in early stages of probable AD. Adding information about Apo ε4 allele frequency slightly enhances diagnostic accuracy. SIGNIFICANCE: qEEG may help identifying patients who are candidates for possible benefit from future disease modifying treatments.
OBJECTIVE: To establish a model for better identification of patients in very early stages of Alzheimer's disease, AD (including patients with amnestic MCI) using high-resolution EEG and genetic data. METHODS: A total of 26 patients in early stages of probable AD and 12 patients with amnestic MCI were included. Both groups were similar in age and education. All patients had a comprehensive neuropsychological examination and a high resolution EEG. Relative band power characteristics were calculated in source space (LORETA inverse solution for spectral data) and compared between groups. A logistic regression model was calculated including relative band-power at the most significant location, ApoE status, age, education and gender. RESULTS: Differences in the delta band at 34 temporo-posterior source locations (p<.01) between AD and MCI groups were detected after correction for multiple comparisons. Classification slightly increased when ApoE status was added (p=.06 maximum likelihood test). Adjustment of analyses for the confounding factors age, gender and education did not alter results. CONCLUSIONS: Quantitative EEG (qEEG) separates between patients with amnestic MCI and patients in early stages of probable AD. Adding information about Apo ε4 allele frequency slightly enhances diagnostic accuracy. SIGNIFICANCE: qEEG may help identifying patients who are candidates for possible benefit from future disease modifying treatments.
Authors: Nina Benz; Florian Hatz; Habib Bousleiman; Michael M Ehrensperger; Ute Gschwandtner; Martin Hardmeier; Stephan Ruegg; Christian Schindler; Ronan Zimmermann; Andreas Urs Monsch; Peter Fuhr Journal: Front Aging Neurosci Date: 2014-11-18 Impact factor: 5.750
Authors: Florian Hatz; Martin Hardmeier; Nina Benz; Michael Ehrensperger; Ute Gschwandtner; Stephan Rüegg; Christian Schindler; Andreas U Monsch; Peter Fuhr Journal: Alzheimers Res Ther Date: 2015-12-31 Impact factor: 6.982
Authors: Lars Michels; Muthuraman Muthuraman; Abdul R Anwar; Spyros Kollias; Sandra E Leh; Florian Riese; Paul G Unschuld; Michael Siniatchkin; Anton F Gietl; Christoph Hock Journal: Front Aging Neurosci Date: 2017-09-20 Impact factor: 5.750
Authors: Daniel J Blackburn; Yifan Zhao; Matteo De Marco; Simon M Bell; Fei He; Hua-Liang Wei; Sarah Lawrence; Zoe C Unwin; Michelle Blyth; Jenna Angel; Kathleen Baster; Thomas F D Farrow; Iain D Wilkinson; Stephen A Billings; Annalena Venneri; Ptolemaios G Sarrigiannis Journal: Brain Sci Date: 2018-07-17