Studying the Alzheimer’s disease continuum using EEG and fMRI in single-modality and multi-modality settings
Author:
Li Jing123, Li Xin123, Chen Futao123, Li Weiping123, Chen Jiu123, Zhang Bing12345
Affiliation:
1. Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School , Nanjing University , Nanjing , Jiangsu , 210008 , China 2. Institute of Medical Imaging and Artificial Intelligence , Nanjing University , Nanjing , Jiangsu , 210008 , China 3. Medical Imaging Center, The Affiliated Drum Tower Hospital , Medical School of Nanjing University , Nanjing , Jiangsu , 210008 , China 4. Jiangsu Key Laboratory of Molecular Medicine , Nanjing , Jiangsu , 210008 , China 5. Institute of Brain Science , Nanjing University , Nanjing , Jiangsu , 210008 , China
Abstract
Abstract
Alzheimer’s disease (AD) is a biological, clinical continuum that covers the preclinical, prodromal, and clinical phases of the disease. Early diagnosis and identification of the stages of Alzheimer’s disease (AD) are crucial in clinical practice. Ideally, biomarkers should reflect the underlying process (pathological or otherwise), be reproducible and non-invasive, and allow repeated measurements over time. However, the currently known biomarkers for AD are not suitable for differentiating the stages and predicting the trajectory of disease progression. Some objective parameters extracted using electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) are widely applied to diagnose the stages of the AD continuum. While electroencephalography (EEG) has a high temporal resolution, fMRI has a high spatial resolution. Combined EEG and fMRI (EEG–fMRI) can overcome single-modality drawbacks and obtain multi-dimensional information simultaneously, and it can help explore the hemodynamic changes associated with the neural oscillations that occur during information processing. This technique has been used in the cognitive field in recent years. This review focuses on the different techniques available for studying the AD continuum, including EEG and fMRI in single-modality and multi-modality settings, and the possible future directions of AD diagnosis using EEG–fMRI.
Publisher
Walter de Gruyter GmbH
Subject
General Neuroscience
Reference142 articles.
1. Aggleton, J.P., Pralus, A., Nelson, A.J., and Hornberger, M. (2016). Thalamic pathology and memory loss in early Alzheimer’s disease: moving the focus from the medial temporal lobe to Papez circuit. Brain 139: 1877–1890, https://doi.org/10.1093/brain/aww083. 2. Aisen, P.S., Cummings, J., Jack, C.R.Jr., Morris, J.C., Sperling, R., Frölich, L., Jones, R.W., Dowsett, S.A., Matthews, B.R., Raskin, J., et al.. (2017). On the path to 2025: understanding the Alzheimer’s disease continuum. Alzheimers Res. Ther. 9: 60, https://doi.org/10.1186/s13195-017-0283-5. 3. Babiloni, C., Carducci, F., Lizio, R., Vecchio, F., Baglieri, A., Bernardini, S., Cavedo, E., Bozzao, A., Buttinelli, C., Esposito, F., et al.. (2013a). Resting state cortical electroencephalographic rhythms are related to gray matter volume in subjects with mild cognitive impairment and Alzheimer’s disease. Hum. Brain Mapp. 34: 1427–1446, https://doi.org/10.1002/hbm.22005. 4. Babiloni, C., Del Percio, C., Boccardi, M., Lizio, R., Lopez, S., Carducci, F., Marzano, N., Soricelli, A., Ferri, R., Triggiani, A.I., et al.. (2015). Occipital sources of resting-state alpha rhythms are related to local gray matter density in subjects with amnesic mild cognitive impairment and Alzheimer’s disease. Neurobiol. Aging 36: 556–570, https://doi.org/10.1016/j.neurobiolaging.2014.09.011. 5. Babiloni, C., Del Percio, C., Bordet, R., Bourriez, J.L., Bentivoglio, M., Payoux, P., Derambure, P., Dix, S., Infarinato, F., Lizio, R., et al.. (2013b). Effects of acetylcholinesterase inhibitors and memantine on resting-state electroencephalographic rhythms in Alzheimer’s disease patients. Clin. Neurophysiol. 124: 837–850, https://doi.org/10.1016/j.clinph.2012.09.017.
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