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 -