Protecting the Aging Brain EEG Dataset

Aging is associated with impaired signaling between brain regions when measured using resting-state fMRI. This age-related destabilization and desynchronization of brain networks reverses itself when the brain switches from metabolizing glucose to ketones. Here, we probe the mechanistic basis for these effects. First, we established their neuronal basis using two datasets acquired from resting-state EEG (Lifespan: standard diet, 20-80 years, N = 201; Metabolic: individually weight-dosed and calorically-matched glucose and ketone ester challenge, μage = 26.9 ± 11.2 years, N = 36). Then, using the multi-scale Larter-Breakspear neural mass model, we identified the unique set of mechanistic parameters consistent with our clinical data. Together, our results implicate potassium (K+) gradient dysregulation as a mechanism for age-related neural desynchronization and its reversal with ketosis, the latter finding of which is consistent with direct measurement of ion channels.

Data and code can be found via Github: https://github.com/lcneuro (global-field-synchronization and brain-network-instability-eeg repositories)

Figure. Schematic of experimental design and methods. (A) Design of within-subject, time-locked targeted metabolic resting-state EEG (rsEEG) experiment. To confirm the neuronal origins of increased brain network stability and synchrony, N = 36 young (µ_age = 26.9 ± 11.2 years), healthy participants underwent four rsEEG scans separated over two days. Following an overnight fast, participants were scanned at baseline and again 30 minutes after consuming a weight-dosed (395 mg/kg) ketone ester or calorie-matched glucose bolus. The rsEEG scans were then repeated using the opposite (ketotic or glycolytic) condition on the second day. (B) An example Global Field Synchronization (GFS) spectrum computed using human rsEEG. The real and imaginary components of Fourier-decomposed rsEEG time-series are plotted on the complex plane for each frequency value. The spread of these points is quantified using principal component analysis. The more the signals are in phase or anti-phase, the greater the difference in magnitude between the first and second principal components of the scatter plot cloud, and the greater the synchrony value (can range from 0 to 1). The scatter points of individual electrodes are color-coded by location on the scalp for illustrative purposes. (C) Schematic characterization of brain network instability. To calculate brain network instability, non-overlapping sliding window correlations are calculated over the entire rsEEG time-series, with strong correlations defining networks. The stability of the networks is then defined as the degree to which these networks persist over time (in units of τ ). (D) Schematic of the Larter-Breakspear neural mass model. Microscale parameters, along with intra-/inter- region coupling (c) and subcortical excitatory inputs (I0) govern the dynamics of the model output: simulated excitatory post-synaptic potentials (EPSPs). The mean EPSPs are multiplied with an EEG lead field to generate simulated EEG time-series, which are used to determine the effects of model parameter variation on synchrony

For more information, see our preprint.