High-power transient 12–30 Hz beta event features as early biomarkers of Alzheimer’s disease conversion: An MEG study

 With your risk of dementia post stroke, do you really think your competent? doctor has EXACT PROTOCOLS TO PREVENT DEMENTIA? Rather than confirming it after the fact? Incompetent doctors will do the confirmation rather than the prevention. I would fire anyone doing biomarker research! It does nothing to get survivors recovered!

The reason you need dementia prevention: 

1. A documented 33% dementia chance post-stroke from an Australian study?   May 2012.

2. Then this study came out and seems to have a range from 17-66%. December 2013.

3. A 20% chance in this research.   July 2013. 

High-power transient 12–30 Hz beta event features as early biomarkers of Alzheimer’s disease conversion: An MEG study

Sophia  GirgentiSophia Girgenti1Isabella  DallastaIsabella Dallasta1Erin  LawrenceErin Lawrence1Dawn  MerbachDawn Merbach1Jonathan  Z SimonJonathan Z Simon2Rafael  H LlinasRafael H Llinas1Neda  F GouldNeda F Gould1Elisabeth  Breese MarshElisabeth Breese Marsh1*

1Johns Hopkins Medicine, Johns Hopkins University, Baltimore, Maryland, United States

2University of Maryland, College Park, College Park, Maryland, United States

The final, formatted version of the article will be published soon.

Danylyna Shpakivska-Bilan Corresponding Author Gianluca Susi David W. Zhou Jesus Cabrera Blanca P. Carvajal Ernesto Pereda Maria Eugenia Lopez Ricardo BruñaFernando Maestu Stephanie R. Jones Author and Article Information Imaging Neuroscience (2025) 3: IMAG.a.69.

https://doi.org/10.1162/IMAG.a.69

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Abstract

A typical pattern observed in M/EEG recordings of mild cognitive impairment (MCI) patients progressing to Alzheimer’s disease (AD) is a continuous slowing of brain oscillatory activity. Definitions of oscillatory slowing are imprecise, as they average across time and frequency bands, masking the finer structure in the signal and potential reliable biomarkers of the disease progression. Recent studies show that high averaged band power can result from transient increases in power, termed “events” or “bursts.” To better understand MEG oscillatory slowing in AD progression, we analyzed features of high-power oscillatory events and their relationship with cognitive decline. MEG resting-state oscillations were recorded in age-matched patients with MCI who later convert (CONV, N = 41) or do not convert (NOCONV, N = 44) to AD, in a period of 2.5 years. To distinguish future CONV from NOCONV, we characterized the rate, duration, frequency span, and power of transient high-power events in the alpha and beta band in two regions of interest in the “X” model of AD progression: anterior cingulate cortex (ACC) and precuneus (PC). Results revealed event-like patterns in resting-state power in both the alpha and beta bands, however, only beta-band features were predictive of conversion to AD, particularly in PC. Specifically, compared with NOCONV, CONV had a lower number of beta events, along with lower power events and a trend toward shorter duration events in PC (⁠⁠). Beta event durations were also significantly shorter in ACC (⁠⁠). Further, this reduced expression of beta events in CONV predicted lower values of mean relative beta power, increased probability of AD conversion, and poorer cognitive performance. Our work paves the way for reinterpreting M/EEG slowing and examining beta event features as a new biomarker along the AD continuum, and we discuss a potential link to theories of inhibitory control in neurodegeneration. These results may bring us closer to understanding the neural mechanisms of the disease that help guide new therapies.

transient high-power events, magnetoencephalography, Alzheimer’s Disease, computational neuroscience, mild cognitive impairment

1 Introduction

According to the World Health Organization 2017, Alzheimer’s disease (AD), a leading cause of disability and dependency among older individuals worldwide, is expected to affect 130 million people by 2050. Despite intensive research efforts, disease-modifying human therapies are still lacking, since the link between amyloid-induced cellular damage and cognitive decline is incomplete (Maestú et al., 2021). Magnetoencephalography (MEG) has been a valuable technique to fill this gap, as it can directly capture human neuronal processes, associated with the disease and cognition, with high temporal resolution (da Silva, 2013; Maestú et al., 2021).

A typical pattern observed in M/EEG recordings of AD patients is a progressive slowing of brain oscillatory activity (Dauwels et al., 2011; Hsiao et al., 2013; Ishii et al., 2017; Jeong, 2004), typically characterized by an increase in low-frequency delta (0.5–4 Hz) and theta rhythms (4–7 Hz), along with a decrease in higher frequency bands, alpha (8–12 Hz) and beta (12–30 Hz) rhythms. This oscillatory slowing initiates in early stages of the disease, such as mild cognitive impairment (MCI) (Babiloni et al., 2004, 2009, 2010; Dauwels et al., 2011; Jelic et al., 2000), and may even manifest before, in the subjective cognitive decline stage (Bruña et al., 2023; López-Sanz et al., 2016), progressing from anterior to posterior cortices and particularly in frontal and parietal regions, (Huang et al., 2000; Nakamura et al., 2018), in line with the onset of amyloid accumulation in the fronto-temporal association cortices (Bang et al., 2015; Cho et al., 2016; Wiesman et al., 2022). The “X” model of AD progression proposes that MCI patients who finally convert to AD exhibit a significant disruption (i.e., decrease in synchronization; König et al., 2005; López-Sanz et al., 2017; Pusil, Dimitriadis, et al., 2019) between anterior cingulate cortex (ACC) and precuneus (PC), two default mode network (DMN) hubs typically involved in the spreading of amyloid beta (Forsberg et al., 2008; Hampel et al., 2021; Sepulcre et al., 2018) and tau (Hampel et al., 2021; Tekin et al., 2001) in the human cortex. As MEG oscillatory slowing accelerates, cognitive decline worsens producing alterations in memory processes and executive functions (Hoshi et al., 2022; Wiesman et al., 2022).

Definitions of oscillatory slowing are imprecise, as they typically rely on methods based on a spectral decomposition followed by averaging across time, frequency bands, and often subjects. Such averaging can mask finer structure in the signal that may provide more reliable biomarkers of the disease progression and cognitive decline and help connect human biomarkers to the underlying neural mechanisms of the disease including possible connections to hyperexcitability as shown in animal models (Maestú et al., 2021; Stoiljkovic et al., 2018; Zott et al., 2019). In recent years, there has been a shift in spectral M/EEG methods, as many studies have shown that, in non-averaged data, brain oscillations often occur as transient increases in high spectral power, a phenomenon termed oscillatory “bursts” or “events” (Jones, 2016; Lundqvist et al., 2024; van Ede et al., 2018). Quantifying transient changes in spectral activity requires new methods that consider temporal characteristics of spectral activity such as event rate, amplitude, duration, or frequency span (Shin et al., 2017). Such event-based methods have recently been applied in a growing body of M/EEG studies on the brain dynamics of cognitive processes (Kavanaugh et al., 2023, 2024; McKeon et al., 2023; Morris et al., 2023; Quinn et al., 2019; Shin et al., 2017), helping to establish neural correlates of cognitive behavior on a single trial level. Variability in oscillatory event parameters may represent a new set of explainable MEG biomarkers for AD progression, as it can reflect differences in circuit-level origins and provide insights into the underlying activity patterns and functions (Jones, 2016; Lundqvist et al., 2024; M. A. Sherman et al., 2016).

In this study, we applied standard power spectral density (PSD) and event-based analysis methods to resting-state MEG from adults with MCI who later convert (CONV) or do not convert (NOCONV) to AD. Motivated by the findings of the AD continuum model described by Pusil, López, et al. (2019) (namely the “X” model), we first hypothesized that averaged PSD slowing exhibits divergent effects in features of high-power transient spectral events. Second, we hypothesized that slowing-related effects in spectral event features would be associated with cognitive decline, as measured by a battery of neuropsychological tests in memory and executive functions in the MCI sample. We characterize MEG oscillatory slowing in terms of transient spectral event parameters in an MCI-to-AD longitudinal sample, taking the initial step toward the potential identification of biophysically principled biomarkers.

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