Spatially resolved neural slowing predicts impairment and amyloid burden in Alzheimer’s disease

Abstract

An extensive electrophysiological literature has proposed a pathological “slowing” of neuronal activity in patients on the Alzheimer’s disease (AD) spectrum. Supported by numerous studies reporting increases in low frequency and decreases in high frequency neural oscillations, this pattern has been suggested as a stable biomarker with potential clinical utility. However, no spatially-resolved metric of such slowing exists, stymieing efforts to understand its relation to proteinopathy and clinical outcomes. Further, the assumption that this slowing is occurring in spatially overlapping populations of neurons has not been empirically validated. In the current study, we collected cross-sectional resting state measures of neuronal activity using magnetoencephalography (MEG) from 38 biomarker-confirmed patients on the AD spectrum and 20 cognitively-normal (CN) biomarker-negative older adults. From these data, we compute and validate a new metric of spatially resolved oscillatory deviations from healthy aging for each patient on the AD spectrum. Using this Pathological Oscillatory Slowing Index (POSI), we show that patients on the AD spectrum exhibit robust neuronal slowing across a network of temporal, parietal, cerebellar, and prefrontal cortices. This slowing effect is shown to be directly relevant to clinical outcomes, as oscillatory slowing in temporal and parietal cortices significantly predicted both general (i.e. MoCA scores) and domain-specific (i.e. attention, language, and processing speed) cognitive function. Further, regional amyloid-beta (Aβ) accumulation, as measured by quantitative 18F florbetapir PET, robustly predicted the strength of this pathological neural slowing effect, and the strength of this relationship between Aβ burden and neural slowing also predicted attentional impairments across patients. These findings provide empirical support for a spatially overlapping effect of oscillatory neural slowing in biomarker-confirmed patients on the AD spectrum, and link this effect to both regional proteinopathy and cognitive outcomes in a spatially resolved manner. The POSI also represents a novel metric that is of potentially high utility across a number of clinical neuroimaging applications, as oscillatory slowing has also been extensively documented in other patient populations, most notably Parkinson’s disease, with divergent spectral and spatial features.

Understanding brain function in vascular cognitive impairment and dementia with EEG and MEG: A systematic review

“…In this regard, the novel spectral analysis method proposed by Donoghue et al, (2020) , which consists in the parameterization of the neural power spectra into periodic and aperiodic components, deserves special mention. This method has already provided promising results, showing that aperiodic activity changes are strongly associated with aging ( Brady and Bardouille, 2022 , Thuwal et al, 2021 ), and with Alzheimer's dementia ( Wiesman et al, 2022 ). Moreover, most of the studies incorporated in this review report data from resting state conditions.…”

A sagittal gradient of pathological and compensatory effects of neurophysiological slowing in Parkinson’s disease

“…We adapted a recent measure of neurophysiological slowing 52 (Figure 1A) with magnetoencephalography (MEG) data from a large sample of patients with PD (N = 79) and a matched group of healthy older adults (N = 65). We related neurophysiological slowing effects measured in the PD group to detailed clinical and neuropsychological indicators of motor and cognitive deficits, with the hypothesis that stronger cortical slowing is associated with greater clinical impairments.…”

Slowing of Frontocentral Beta Oscillations in Atypical Parkinsonism

“…The changes in power reported here are reminiscent of pathological alterations observed in other neurological diseases such as multiple sclerosis (van Schependom et al 2021), hepatic encephalopathy (Butz et al 2013), and AD. Especially for AD, numerous studies have reported spectral slowing (Fernández et al 2006; Wiesman et al 2022), increased theta activity and decreased alpha and beta activity (Huang et al 2000; Berendse et al 2000; Fernández et al 2002; Osipova et al 2005; Fernández et al 2006; Dauwels et al 2011; Haan et al 2008; Jelic et al 2000; Coben et al 1985; Jafari et al 2020; Nakamura et al 2018; Wiesman et al 2022). These spectral changes have been linked to disease progression (Jelic et al 2000; Coben et al 1985; Huang et al 2000; Babiloni et al 2009; Nakamura et al 2018) and were successfully used to differentiate AD patients from healthy controls (Gouw et al 2021).…”

“…Furthermore, tau accumulation was related to posterior slowing of oscillatory activity in a recent MEG study (Coomans et al 2021) and human-tau-transgenic mice exhibit power increases in the delta and theta band in parietal areas reminiscent of spectral changes observed in AD patients (Das et al 2018). Oscillatory markers were also found to correlate with amyloid-beta accumulation in AD (Nakamura et al 2018; Wiesman et al 2022), a pathological process assumed to precede tau pathology (Buchhave et al 2012; Jack et al 2013), suggesting general sensitivity of oscillatory metrics to proteinopathy (Wiesman et al 2022).…”