Event-related potential and EEG oscillatory predictors of verbal memory in mild cognitive impairment

Evaluating frontoparietal network topography for diagnostic markers of Alzheimer's disease

Numerous prospective biomarkers are being studied for their ability to diagnose various stages of Alzheimer's disease (AD). High-density electroencephalogram (EEG) methods show promise as an accurate, economical, non-invasive approach to measuring the electrical potentials of brains associated with AD. Event-related potentials (ERPs) may serve as clinically useful biomarkers of AD. Through analysis of secondary data, the present study examined the performance and distribution of N4/P6 ERPs across the frontoparietal network (FPN) using EEG topographic mapping. ERP measures and memory as a function of reaction time (RT) were compared between a group of (n = 63) mild untreated AD patients and a control group of (n = 73) healthy age-matched adults. Based on the literature presented, it was expected that healthy controls would outperform patients in peak amplitude and mean component latency across three parameters of memory when measured at optimal N4 (frontal) and P6 (parietal) locations. It was also predicted that the control group would exhibit neural cohesion through FPN integration during cross-modal tasks, thus demonstrating healthy cognitive functioning consistent with older healthy adults. By targeting select frontal and parietal EEG reference channels based on N4/P6 component time windows and positivity, our findings demonstrated statistically significant group variations between controls and patients in N4/P6 peak amplitudes and latencies during cross-modal testing. Our results also support that the N4 ERP might be stronger than its P6 counterpart as a possible candidate biomarker. We conclude through topographic mapping that FPN integration occurs in healthy controls but is absent in AD patients during cross-modal memory tasks.

Electroencephalography and mild cognitive impairment research: A scoping review and bibliometric analysis (ScoRBA)

Mild cognitive impairment (MCI) is often considered a precursor to Alzheimer's disease (AD) and early diagnosis may help improve treatment effectiveness. To identify accurate MCI biomarkers, researchers have utilized various neuroscience techniques, with electroencephalography (EEG) being a popular choice due to its low cost and better temporal resolution. In this scoping review, we analyzed 2310 peer-reviewed articles on EEG and MCI between 2012 and 2022 to track the research progress in this field. Our data analysis involved co-occurrence analysis using VOSviewer and a Patterns, Advances, Gaps, Evidence of Practice, and Research Recommendations (PAGER) framework. We found that event-related potentials (ERP), EEG, epilepsy, quantitative EEG (QEEG), and EEG-based machine learning were the primary research themes. The study showed that ERP/EEG, QEEG, and EEG-based machine learning frameworks provide high-accuracy detection of seizure and MCI. These findings identify the main research themes in EEG and MCI and suggest promising avenues for future research in this field.

Machine Learning on Visibility Graph Features Discriminates the Cognitive Event-Related Potentials of Patients with Early Alzheimer's Disease from Healthy Aging

We present a framework for electroencephalography (EEG)-based classification between patients with Alzheimer's Disease (AD) and robust normal elderly (RNE) via a graph theory approach using visibility graphs (VGs). This EEG VG approach is motivated by research that has demonstrated differences between patients with early stage AD and RNE using various features of EEG oscillations or cognitive event-related potentials (ERPs). In the present study, EEG signals recorded during a word repetition experiment were wavelet decomposed into 5 sub-bands (δ,θ,α,β,γ). The raw and band-specific signals were then converted to VGs for analysis. Twelve graph features were tested for differences between the AD and RNE groups, and t-tests employed for feature selection. The selected features were then tested for classification using traditional machine learning and deep learning algorithms, achieving a classification accuracy of 100% with linear and non-linear classifiers. We further demonstrated that the same features can be generalized to the classification of mild cognitive impairment (MCI) converters, i.e., prodromal AD, against RNE with a maximum accuracy of 92.5%. Code is released online to allow others to test and reuse this framework.

Predictive Power of Cognitive Biomarkers in Neurodegenerative Disease Drug Development: Utility of the P300 Event-Related Potential

Neurodegenerative diseases, such as Alzheimer's disease (AD), and their associated deterioration of cognitive function are common causes of disability. The slowly developing pathology of neurodegenerative diseases necessitates early diagnosis and monitored long-term treatment. Lack of effective therapies coupled with an improved rate of early diagnosis in our aging population have created an urgent need for the development of novel drugs, as well as the need for reliable biomarkers for treatment response. These issues are especially relevant for AD, in which the rate of clinical trial drug failures has been very high. Frequently used biomarker evaluation procedures, such as positron emission tomography or cerebrospinal fluid measurements of phospho-tau and amyloid beta, are invasive and costly, and not universally available or accessible. This review considers the functionality of the event-related potential (ERP) P300 methodology as a surrogate biomarker for predicting the procognitive potential of drugs in clinical development for neurocognitive disorders. Through the application of standardized electroencephalography (EEG) described here, ERP P300 can be reliably measured. The P300 waveform objectively measures large-scale neuronal network functioning and working memory processes. Increased ERP P300 latency has been reported throughout the literature in disorders of cognition, supporting the potential utility of ERP P300 as a biomarker in many neurological and neuropsychiatric disorders, including AD. Specifically, evidence presented here supports ERP P300 latency as a quantitative, unbiased measure for detecting changes in cognition in patients with AD dementia through the progression from mild to moderate cognitive impairment and after drug treatment.

Non-zero mean alpha oscillations revealed with computational model and empirical data

Ongoing oscillations and evoked responses are two main types of neuronal activity obtained with diverse electrophysiological recordings (EEG/MEG/iEEG/LFP). Although typically studied separately, they might in fact be closely related. One possibility to unite them is to demonstrate that neuronal oscillations have non-zero mean which predicts that stimulus- or task-triggered amplitude modulation of oscillations can contribute to the generation of evoked responses. We validated this mechanism using computational modelling and analysis of a large EEG data set. With a biophysical model, we indeed demonstrated that intracellular currents in the neuron are asymmetric and, consequently, the mean of alpha oscillations is non-zero. To understand the effect that neuronal currents exert on oscillatory mean, we varied several biophysical and morphological properties of neurons in the network, such as voltage-gated channel densities, length of dendrites, and intensity of incoming stimuli. For a very large range of model parameters, we observed evidence for non-zero mean of oscillations. Complimentary, we analysed empirical rest EEG recordings of 90 participants (50 young, 40 elderly) and, with spatio-spectral decomposition, detected at least one spatially-filtred oscillatory component of non-zero mean alpha oscillations in 93% of participants. In order to explain a complex relationship between the dynamics of amplitude-envelope and corresponding baseline shifts, we performed additional simulations with simple oscillators coupled with different time delays. We demonstrated that the extent of spatial synchronisation may obscure macroscopic estimation of alpha rhythm modulation while leaving baseline shifts unchanged. Overall, our results predict that amplitude modulation of neural oscillations should at least partially explain the generation of evoked responses. Therefore, inference about changes in evoked responses with respect to cognitive conditions, age or neuropathologies should be constructed while taking into account oscillatory neuronal dynamics.

Individual differences in behavioral and electrophysiological signatures of familiarity- and recollection-based recognition memory

Our everyday memories can vary in terms of accuracy and phenomenology. According to one theoretical account, these differences hinge on whether the memories contain information about both an item itself as well as associated details (remember) versus those that are devoid of these associated contextual details (familiar). This distinction has been supported by computational modeling of behavior, studies in patients, and neuroimaging work including differences both in electrophysiological and functional magnetic resonance imaging. At present, however, little evidence has emerged to suggest that neurophysiological measures track individual differences in estimates of recollection and familiarity. Here, we conducted electrophysiological recordings of brain activity during a recognition memory task designed to differentiate between behavioral indices of recollection and familiarity. Non-parametric cluster-based permutation analyses revealed associations between electrophysiological signatures of familiarity and recollection with their respective behavioral estimates. These results support the idea that recollection and familiarity are distinct phenomena and is the first, to our knowledge, to identify distinct electrophysiological signatures that track individual differences in these processes.

The Effect of Prior Knowledge of Color on Behavioral Responses and Event-Related Potentials During Go/No-go Task

In daily life, the meaning of color plays an important role in execution and inhibition of a motor response. For example, the symbolism of traffic light can help pedestrians and drivers to control their behavior, with the color green/blue meaning go and red meaning stop. However, we don't always stop with a red light and sometimes start a movement with it in such a situation as drivers start pressing the brake pedal when a traffic light turns red. In this regard, we investigated how the prior knowledge of traffic light signals impacts reaction times (RTs) and event-related potentials (ERPs) in a Go/No-go task. We set up Blue Go/Red No-go and Red Go/Blue No-go tasks with three different go signal (Go) probabilities (30, 50, and 70%), resulting in six different conditions. The participants were told which color to respond (Blue or Red) just before each condition session but didn't know the Go probability. Neural responses to Go and No-go signals were recorded at Fz, Cz, and Oz (international 10-20 system). We computed RTs for Go signal and N2 and P3 amplitudes from the ERP data. We found that RT was faster when responding to blue than red light signal and also was slower with lower Go probability. Overall, N2 amplitude was larger in Red Go than Blue Go trial and in Red No-go than Blue No-go trial. Furthermore, P3 amplitude was larger in Red No-go than Blue No-go trial. Our findings of RT and N2 amplitude for Go ERPs could indicate the presence of Stroop-like interference, that is a conflict between prior knowledge about traffic light signals and the meaning of presented signal. Meanwhile, the larger N2 and P3 amplitudes in Red No-go trial as compared to Blue No-go trial may be due to years of experience in stopping an action in response to a red signal and/or attention. This study provides the better understanding of the effect of prior knowledge of color on behavioral responses and its underlying neural mechanisms.