Regular articleEEG spectral analysis as a putative early prognostic biomarker in nondemented, amyloid positive subjects
Introduction
Alzheimer's disease (AD) develops gradually over the course of 15–20 years. One of the first pathological changes of the disease is the accumulation of amyloid beta in the brain, which starts many years before the appearance of first symptoms of cognitive decline (Jack et al., 2013). Identifying subjects in the earliest stages of the disease offers the opportunity to apply potential preventive measures, before neurodegeneration and synapse loss are irreversible. Recent research criteria have taken amyloid-β 1-42 concentration in CSF and amyloid PET imaging into account to support the diagnosis of AD in subjects with and without dementia (Albert et al., 2011, Dubois et al., 2014, McKhann et al., 2011, Sperling et al., 2011).
Synaptic dysfunction resulting from synaptic toxicity of amyloid beta supposedly occurs early in the cascade of events eventually leading to cognitive decline and dementia (Palop and Mucke, 2010, Selkoe, 2002, Sperling et al., 2013). The most clinically relevant method to capture in vivo synaptic functioning is electroencephalography (EEG) that directly measures postsynaptic dendritic currents of synchronized cortical neurons. Previous EEG studies in patients with dementia due to AD show a gradual diffuse slowing of brain electrical activity reflected by theta power increases and beta power decreases, followed in later stages by a decrease in alpha power and increase in delta power (de Haan et al., 2008, Jeong, 2004, van Straaten et al., 2014). At the mild cognitive impairment (MCI) stage, EEG abnormalities are intermediate between healthy controls and dementia patients (Kwak, 2006, van der Hiele et al., 2007). Several longitudinal studies in MCI have suggested that EEG measures are associated with incident clinical progression over 1–3 years (Huang et al., 2000, Jelic et al., 2000, Luckhaus et al., 2008). In subjects with subjective cognitive decline, only one study has been performed and reported prediction of decline to MCI by several spectral and covariance measures, predominantly in the theta band (Prichep et al., 2006). However, these studies did not take into account the underlying pathology, in particular amyloid status, in their study populations, so it remains unclear if these subjects belonged to the AD pathophysiological continuum at all. To answer questions about prognosis in the nondementia phases of AD, it is therefore crucial to select subjects who have biomarker proof of underlying Alzheimer's pathology (Giannakopoulos et al., 2009, Sperling et al., 2011).
Although it has been demonstrated that subjects with positive amyloid markers in the preclinical and MCI stages have an increased risk to develop dementia compared with amyloid negative subjects (Van Harten et al., 2013), relationships with severity of impairment are modest and its prognostic value for predicting time to dementia is quite limited (Prestia et al., 2015, van Rossum et al., 2012b). Additional markers sensitive enough to predict cognitive decline are required. For the effective design of prevention trials in AD that are targeted against amyloid pathology, these prognostic markers and the selection of subjects with proven amyloid pathology are crucial (Hampel et al., 2011).
Here, we studied in nondemented subjects with an amyloid positive biomarker status, whether EEG-derived measures of brain oscillatory activity are associated with clinical progression. We hypothesized that diffuse slowing of oscillatory activity, reflected by increased relative power in the lower frequency bands (theta and delta) and decreased relative power in higher frequency bands (alpha and beta), is related to clinical progression.
Section snippets
Subjects
We included 205 nondemented, amyloid positive subjects from the Amsterdam Dementia Cohort (van der Flier et al., 2014). All subjects were referred to the Alzheimer Center between February 2001 and January 2014. They underwent a standardized screening including medical history, informant based history, physical and neurological examination, neuropsychological evaluation, EEG, magnetic resonance imaging, laboratory tests, and lumbar puncture. All diagnoses were made in a multidisciplinary
Baseline characteristics
In total, 205 patients (mean age 67.6 ± 7.7 years; 103 females) were included and followed during a median period of 2.2 years (Table 1). Of these patients, 63 had a diagnosis of SCD and 142 had a diagnosis of MCI at baseline. Subjects with MCI were more often tau-positive with higher CSF tau and p-tau levels than SCD patients. Groups did not differ with regard to age, gender, level of education, APOE e4 status, medial temporal lobe atrophy (MTA) score (Scheltens et al., 1995), CSF amyloid-β
Discussion
The main finding of our study is that EEG-derived measures of brain oscillatory activity were related to clinical progression at very early stages of AD, when objective cognitive impairment has not yet occurred.
The SCD patients included in this study visited our memory clinic with cognitive complaints but had no objective cognitive impairment. Experiencing subjective cognitive complaints is a risk factor for developing AD, especially combined with biomarker evidence for amyloid positivity (
Conclusions
In conclusion, EEG spectral measures were related to clinical progression in nondemented subjects with proven amyloid pathology, specifically in the very early stages of the disease, when cognitive disturbances have not yet occurred.
Disclosure statement
Ph. Scheltens has received grant support (for the institution) from GE Healthcare, Danone Research, Piramal and Merck. In the past 2 years, the author has received consultancy/speaker fees (paid to the institution) from Lilly, GE Healthcare, Novartis, Forum, Sanofi, Nutricia, Probiodrug and EIP Pharma. Research programs of W. van der Flier have been funded by ZonMW, NWO, EU-FP7, Alzheimer Nederland, CardioVascular Onderzoek Nederland, stichting Dioraphte, Gieskes-Strijbis fonds, Boehringer
Acknowledgements
Research of the VUmc Alzheimer Center is part of the neurodegeneration research program of the Neuroscience Campus Amsterdam. The VUmc Alzheimer Center is supported by Stichting Alzheimer Nederland and Stichting VUmc fonds. The clinical database structure was developed with funding from Stichting Dioraphte. This study was funded by a research grant of Boehringer Ingelheim Pharma GmbH Co KG, Germany. The authors acknowledge Meichen Yu for his help with the figure.
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