Skip to main content Accessibility help
×
Hostname: page-component-848d4c4894-4rdrl Total loading time: 0 Render date: 2024-06-26T18:38:50.814Z Has data issue: false hasContentIssue false

36 - The Role of Electroencephalography in Alzheimer’s Disease Drug Development

from Section 4 - Imaging and Biomarker Development in Alzheimer’s Disease Drug Discovery

Published online by Cambridge University Press:  03 March 2022

Jeffrey Cummings
Affiliation:
University of Nevada, Las Vegas
Jefferson Kinney
Affiliation:
University of Nevada, Las Vegas
Howard Fillit
Affiliation:
Alzheimer’s Drug Discovery Foundation
Get access

Summary

The clinical value of EEG in Alzheimer’s disease (AD) trials is increasingly recognized, offering a practical, patient-friendly assessment of neurophysiological response to novel treatment. Its non-invasive, task-independent, and relatively straightforward mode of operation make it a suitable candidate for longitudinal trials in patients with cognitive impairment. The visual analysis in EEG has led to the well-described process of diffuse oscillatory slowing in AD. It is complemented by advanced quantitative analysis methods, giving a more accurate and diverse overview along the AD disease course, such as loss of functional connectivity and functional network structure. Many of these neurophysiological changes are linked to AD pathology and cognitive decline, and recent trials have implicated the practical feasibility and potency of EEG-based markers. In this chapter, we discuss what EEG analysis techniques are most useful for AD research, the hallmark EEG changes in AD, and insights from recent trials assessing the effect of new compounds on EEG activity. We offer a practical view on the most essential elements for obtaining consistent data quality in multi-center trials.

Type
Chapter
Information
Alzheimer's Disease Drug Development
Research and Development Ecosystem
, pp. 418 - 428
Publisher: Cambridge University Press
Print publication year: 2022

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Berger, H. Über das Elektrenkephalogramm des Menschen. Dritte Mitteilung. Arch Psychiatr Nervenkr 1931; 94: 1660.CrossRefGoogle Scholar
Berger, H. Über das Elektrenkephalogramm des Menschen. Fünfte Mitteilung. Arch Psychiatr Nervenkr 1932; 98: 231–54.Google Scholar
Babiloni, C, Blinowska, K, Bonanni, L, et al. What electrophysiology tells us about Alzheimer’s disease: a window into the synchronization and connectivity of brain neurons. Neurobiol Aging 2020; 85: 5873.Google Scholar
Palop, JJ, Mucke, L. Network abnormalities and interneuron dysfunction in Alzheimer disease. Nat Rev Neurosci 2016; 17: 777–92.CrossRefGoogle ScholarPubMed
Styr, B, Slutsky, I. Imbalance between firing homeostasis and synaptic plasticity drives early-phase Alzheimer’s disease. Nat Neurosci 2018; 21: 463–73.CrossRefGoogle ScholarPubMed
Canter, RG, Penney, J, Tsai, LH. The road to restoring neural circuits for the treatment of Alzheimer’s disease. Nature 2016; 539: 187–96.Google Scholar
Selkoe, DJ. Alzheimer’s disease is a synaptic failure. Science 2002; 298: 789–91.CrossRefGoogle ScholarPubMed
D’Amelio, M, Rossini, PM. Brain excitability and connectivity of neuronal assemblies in Alzheimer’s disease: from animal models to human findings. Prog Neurobiol 2012; 99: 4260.Google Scholar
van Straaten, EC, Scheltens, P, Gouw, AA, Stam, CJ. Eyes-closed task-free electroencephalography in clinical trials for Alzheimer’s disease: an emerging method based upon brain dynamics. Alzheimers Res Ther 2014; 6: 86.Google Scholar
Delbeuck, X, Van der Linder, M, Colette, F. Alzheimer’s disease as a disconnection syndrome? Neuropyschol Rev 2003; 13: 7992.Google Scholar
Briels, CT, Schoonhoven, DN, Stam, CJ, et al. Reproducibility of EEG functional connectivity in Alzheimer’s disease. Alzheimers Res Ther 2020; 12 : 68.Google Scholar
Stam, CV, Van Straaten, ECW. The organization of physiological brain networks. Clin Neurophysiol 2012; 123: 1067–87.Google Scholar
Rossini, PM, Rossi, S, Babiloni, C, Polich, J. Clinical neurophysiology of aging brain: from normal aging to neurodegeneration. Prog Neurobiol 2007; 83: 375400.CrossRefGoogle ScholarPubMed
Prichep, LS, John, ER, Ferris, SH, et al. Prediction of longitudinal cognitive decline in normal elderly with subjective complaints using electrophysiological imaging. Neurobiol Aging 2006; 27: 471–81.CrossRefGoogle ScholarPubMed
Gouw, AA, Alsema, AM, Tijms, BM, et al. EEG spectral analysis as a putative early prognostic biomarker in nondemented, amyloid positive subjects. Neurobiol Aging 2017; 57: 133–42.Google Scholar
Jelic, V, Johansson, S-E, Almkvist, O, et al. Quantitative electroencephalography in mild cognitve impairment: longitudinal changes and possible prediction of Alzheimer’s disease. Neurobiol Aging 2000; 21: 533–40.Google Scholar
van der Hiele, K, Bollen, EL, Vein, AA, et al. EEG markers of future cognitive performance in the elderly. J Clin Neurophysiol 2008; 25: 83–9.CrossRefGoogle ScholarPubMed
Liedorp, M, van der Flier, WM, Hoogervorst, EL, Scheltens, P, Stam, CJ. Associations between patterns of EEG abnormalities and diagnosis in a large memory clinic cohort. Dement Geriatr Cogn Disord 2008; 27: 1823.Google Scholar
Jeong, J. EEG dynamics in patients with Alzheimer’s disease. Clin Neurophysiol 2004; 115: 1490–505.Google Scholar
Claus, JJ, Ongerboer de Visser, BW, Walstra, GJM, et al. Quantitative spectral electroencephalography in predicting survival in patients with early Alzheimer disease. Arch Neurol 1998; 55: 1105–11.Google Scholar
Stam, CJ. Nonlinear dynamical analysis of EEG and MEG: review of an emerging field. Clin Neurophysiol 2005; 116: 2266–301.CrossRefGoogle ScholarPubMed
Stam, CJ. Modern network science of neurological disorders. Nat Rev Neurosci 2014; 15: 683–95.CrossRefGoogle ScholarPubMed
Stam, CJ, Jones, BF, Nolte, G, Breakspear, M, Scheltens, P. Small-world networks and functional connectivity in Alzheimer’s disease. Cereb Cortex 2007; 17: 92–9.Google ScholarPubMed
Sun, J, Wang, B, Niu, Y, et al. Complexity analysis of EEG, MEG, and fMRI in mild cognitive impairment and Alzheimer’s disease: a review. Entropy 2020; 22: 239.CrossRefGoogle ScholarPubMed
Dauwels, J, Vialatte, F, Cichocki, A. Diagnosis of Alzheimer’s disease from EEG signals: where are we standing? Curr Alzheimer Res 2010; 7: 487505.Google Scholar
Simpraga, S, Alvarez-Jimenez, R, Mansvelder, HD, et al. EEG machine learning for accurate detection of cholinergic intervention and Alzheimer’s disease. Sci Rep 2017; 7: 111.Google Scholar
Vecchio, F, Miraglia, F, Alù, F, et al. Classification of Alzheimer’s disease with respect to physiological aging with innovative EEG biomarkers in a machine learning implementation. J Alzheimers Dis 2020; 75: 1253–61.Google Scholar
Dauwan, M, van der Zande, JJ, van Dellen, E, et al. Random forest to differentiate dementia with Lewy bodies from Alzheimer’s disease. Alzheimers Dement (Amst) 2016; 4: 99106.Google Scholar
Van der Flier, WM, Scheltens, P. Use of laboratory and imaging investigations in dementia. J Neurol Neurosurg Psychiatry 2005; 76: v4552.Google Scholar
Drago, V, Babiloni, C, Bartrés-Faz, D, et al. Disease tracking markers for Alzheimer’s disease at the prodromal (MCI) stage. J Alzheimers Dis 2011; 26: 159–99.Google Scholar
Rossini, PM, Di Iorio, R, Vecchio, F, et al. Early diagnosis of Alzheimer’s disease: the role of biomarkers including advanced EEG signal analysis. Report from the IFCN-sponsored panel of experts. Clin Neurophysiol 2020; 131: 1287–310.Google Scholar
Stam, CJ, van der Made, Y, Pijnenburg, YAL, Scheltens, Ph. EEG synchronization in mild cognitive impairment and Alzheimer’s disease. Acta Neurol Scand 2003; 108: 90–6.CrossRefGoogle ScholarPubMed
Riekkinen, P, Buzsaki, G, Jr, Riekkinen P., Soininen, H, Partanen, J. The cholinergic system and EEG slow waves. Electroenceph Clin Neurophysiol 1991; 78: 8996.Google Scholar
Babiloni, C, Cassetta, E, Dal Forno, G, et al. Donepezil effects on sources of cortical rhythms in mild Alzheimer’s disease: responders vs. non-responders. Neuroimage 2006; 31: 1650–65.Google Scholar
Adler, G, Brassen, S, Chwalek, K, Dieter, B, Teufel, M. Prediction of treatment response to rivastigmine in Alzheimer’s dementia. J Neurol Neurosurg Psychiatry 2004; 75: 292–4.Google Scholar
Jelic, V, Blomberg, M, Dierks, T, et al. EEG slowing and cerebrospinal fluid tau levels in patients with cognitive decline. Neuroreport 1988; 9: 157–60.Google Scholar
Grunwald, M, Hensel, A, Wolf, H, Weiss, T, Gertz, HJ. Does the hippocampal atrophy correlate with the cortical theta power in elderly subjects with a range of cognitive impairment? J Clin Neurophysiol 2007; 24: 22–6.Google Scholar
Ponomareva, NV, Korovaitseva, GI, Rogaev, EI. EEG alterations in non-demented individuals related to apolipoprotein E genotype and to risk of Alzheimer disease. Neurobiol Aging 2008; 29: 819–27.Google Scholar
Babiloni, C, Del Percio, C, Bordet, R, et al. Effects of acetylcholinesterase inhibitors and memantine on resting-state electroencephalographic rhythms in Alzheimer’s disease patients. Clin Neurophysiol 2013; 124: 837–50.Google Scholar
Scheltens, P, Hallikainen, M, Grimmer, T, et al. Safety, tolerability and efficacy of the glutaminyl cyclase inhibitor PQ912 in Alzheimer’s disease: results of a randomized, double-blind, placebo-controlled Phase 2a study. Alzheimers Res Ther 2018; 10: 107.Google Scholar
Briels, CT, Stam, CJ, Scheltens, P, et al. In pursuit of a sensitive EEG functional connectivity outcome measure for clinical trials in Alzheimer’s disease. Clin Neurophysiol 2020; 131: 8895.CrossRefGoogle ScholarPubMed
de Waal, H, Stam, CJ, Lansbergen, MM, et al. The effect of Souvenaid on functional brain network organisation in patients with mild Alzheimer’s disease: a randomised controlled study. PLoS One 2014; 9: e86558.Google Scholar
Scheltens, P, Twisk, JW, Blesa, R, et al. Efficacy of Souvenaid in mild Alzheimer’s disease: results from a randomized, controlled trial. J Alzheimers Dis 2012; 31: 225–36.CrossRefGoogle ScholarPubMed
Cassani, R, Estarellas, M, San-Martin, R, Fraga, FJ, Falk, TH. Systematic review on resting-state EEG for Alzheimer’s disease diagnosis and progression assessment. Dis Markers 2018; 2018: 5174815.CrossRefGoogle ScholarPubMed

Save book to Kindle

To save this book to your Kindle, first ensure coreplatform@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×