Neuroimaging markers of global cognition in early Alzheimer's disease: A magnetic resonance imaging-electroencephalography study
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Neuroimaging markers of global cognition in early Alzheimer's disease : A magnetic resonance imaging-electroencephalography study. / Waser, Markus; Benke, Thomas; Dal-Bianco, Peter; Garn, Heinrich; Mosbacher, Jochen A; Ransmayr, Gerhard; Schmidt, Reinhold; Seiler, Stephan; Sorensen, Helge B D; Jennum, Poul J.
In: Brain and Behavior, Vol. 9, No. 1, e01197, 01.2019.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - Neuroimaging markers of global cognition in early Alzheimer's disease
T2 - A magnetic resonance imaging-electroencephalography study
AU - Waser, Markus
AU - Benke, Thomas
AU - Dal-Bianco, Peter
AU - Garn, Heinrich
AU - Mosbacher, Jochen A
AU - Ransmayr, Gerhard
AU - Schmidt, Reinhold
AU - Seiler, Stephan
AU - Sorensen, Helge B D
AU - Jennum, Poul J
N1 - © 2018 The Authors. Brain and Behavior published by Wiley Periodicals, Inc.
PY - 2019/1
Y1 - 2019/1
N2 - INTRODUCTION: Magnetic resonance imaging (MRI) and electroencephalography (EEG) are a promising means to an objectified assessment of cognitive impairment in Alzheimer's disease (AD). Individually, however, these modalities tend to lack precision in both AD diagnosis and AD staging. A joint MRI-EEG approach that combines structural with functional information has the potential to overcome these limitations.MATERIALS AND METHODS: This cross-sectional study systematically investigated the link between MRI and EEG markers and the global cognitive status in early AD. We hypothesized that the joint modalities would identify cognitive deficits with higher accuracy than the individual modalities. In a cohort of 111 AD patients, we combined MRI measures of cortical thickness and regional brain volume with EEG measures of rhythmic activity, information processing and functional coupling in a generalized multiple regression model. Machine learning classification was used to evaluate the markers' utility in accurately separating the subjects according to their cognitive score.RESULTS: We found that joint measures of temporal volume, cortical thickness, and EEG slowing were well associated with the cognitive status and explained 38.2% of ifs variation. The inclusion of the covariates age, sex, and education considerably improved the model. The joint markers separated the subjects with an accuracy of 84.7%, which was considerably higher than by using individual modalities.CONCLUSIONS: These results suggest that including joint MRI-EEG markers may be beneficial in the diagnostic workup, thus allowing for adequate treatment. Further studies in larger populations, with a longitudinal design and validated against functional-metabolic imaging are warranted to confirm the results.
AB - INTRODUCTION: Magnetic resonance imaging (MRI) and electroencephalography (EEG) are a promising means to an objectified assessment of cognitive impairment in Alzheimer's disease (AD). Individually, however, these modalities tend to lack precision in both AD diagnosis and AD staging. A joint MRI-EEG approach that combines structural with functional information has the potential to overcome these limitations.MATERIALS AND METHODS: This cross-sectional study systematically investigated the link between MRI and EEG markers and the global cognitive status in early AD. We hypothesized that the joint modalities would identify cognitive deficits with higher accuracy than the individual modalities. In a cohort of 111 AD patients, we combined MRI measures of cortical thickness and regional brain volume with EEG measures of rhythmic activity, information processing and functional coupling in a generalized multiple regression model. Machine learning classification was used to evaluate the markers' utility in accurately separating the subjects according to their cognitive score.RESULTS: We found that joint measures of temporal volume, cortical thickness, and EEG slowing were well associated with the cognitive status and explained 38.2% of ifs variation. The inclusion of the covariates age, sex, and education considerably improved the model. The joint markers separated the subjects with an accuracy of 84.7%, which was considerably higher than by using individual modalities.CONCLUSIONS: These results suggest that including joint MRI-EEG markers may be beneficial in the diagnostic workup, thus allowing for adequate treatment. Further studies in larger populations, with a longitudinal design and validated against functional-metabolic imaging are warranted to confirm the results.
KW - Aged
KW - Aged, 80 and over
KW - Alzheimer Disease/diagnostic imaging
KW - Biomarkers
KW - Cognition/physiology
KW - Cognitive Dysfunction/diagnostic imaging
KW - Cross-Sectional Studies
KW - Disease
KW - Electroencephalography
KW - Female
KW - Humans
KW - Machine Learning
KW - Magnetic Resonance Imaging
KW - Male
KW - Neuroimaging/methods
U2 - 10.1002/brb3.1197
DO - 10.1002/brb3.1197
M3 - Journal article
C2 - 30592179
VL - 9
JO - Brain and Behavior
JF - Brain and Behavior
SN - 2157-9032
IS - 1
M1 - e01197
ER -
ID: 234700094