Abstract
The detection of mild cognitive impairment (MCI), the transitional stage between normal cognitive changes of aging and the cognitive decline caused by AD, is of paramount clinical importance, since MCI patients are at increased risk of progressing into AD. Electroencephalographic (EEG) alterations in the spectral content of brainwaves and connectivity at resting state have been associated with early-stage AD. Recently, cognitive event-related potentials (ERPs) have entered into the picture as an easy to perform screening test. Motivated by the recent findings about the role of cross-frequency coupling (CFC) in cognition, we introduce a relevant methodological approach for detecting MCI based on cognitive responses from a standard auditory oddball paradigm. By using the single trial signals recorded at Pz sensor and comparing the responses to target and non-target stimuli, we first demonstrate that increased CFC is associated with the cognitive task. Then, considering the dynamic character of CFC, we identify instances during which the coupling between particular pairs of brainwave frequencies carries sufficient information for discriminating between normal subjects and patients with MCI. In this way, we form a multiparametric signature of impaired cognition. The new composite biomarker was tested using data from a cohort that consists of 25 amnestic MCI patients and 15 age-matched controls. Standard machine-learning algorithms were employed so as to implement the binary classification task. Based on leave-one-out cross-validation, the measured classification rate was found reaching very high levels (95%). Our approach compares favorably with the traditional alternative of using the morphology of averaged ERP response to make the diagnosis and the usage of features from spectro-temporal analysis of single-trial responses. This further indicates that task-related CFC measurements can provide invaluable analytics in AD diagnosis and prognosis.
Highlights
Alzheimer’s disease (AD) is a neuro-degenerative disorder, characterized by loss of memory and declined cognitive and intellectual abilities, that severely affects patients’ social life but even their daily living
Among the available phase-amplitude coupling (PAC) estimators, we adopted the one based on the phase coherence measure (Cohen, 2008; Voytek et al, 2010) and further adapted it so as to operate across trials and provide time-resolved profiles of cross-frequency coupling (CFC) that would be studied in relation with the established components of the cognitive response (N2, P300, SW)
The section begins with the presentation of the Grand-Averaged responses from the two groups, so as to provide an indication about the difficulty of the problem of discrimination between amnestic MCI (aMCI) patients and NI-controls based on the temporal patterning of the time-locked responses
Summary
Alzheimer’s disease (AD) is a neuro-degenerative disorder, characterized by loss of memory and declined cognitive and intellectual abilities, that severely affects patients’ social life but even their daily living. The diagnosis of AD is performed via clinical neuropsychological tests with accuracies ranging from 85 to 93% This widely-used procedure requires long sessions in hospitals and the involvement of experienced staff (Paajanen et al, 2014). For this reason, the definition of a reliable, low cost and, preferably, non-invasive biomarker for the early diagnosis of AD is an active research area. The definition of a reliable, low cost and, preferably, non-invasive biomarker for the early diagnosis of AD is an active research area Toward this end electroencephalography (EEG) has been adopted as a potential screening method, since functional alterations due to AD most probably are reflected in the recorded cerebral activity of a patient (Ponomareva et al, 2013). In a more recent study, the quantification of crossfrequency amplitude-to-amplitude modulations during restingstate was introduced as a means of differentiating patients with mild AD symptoms from patients with moderate symptoms (Fraga et al, 2013)
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