Resting-state oscillations reveal disturbed excitation–inhibition ratio in Alzheimer’s disease patients

Abstract

An early disruption of neuronal excitation–inhibition (E–I) balance in preclinical animal models of Alzheimer’s disease (AD) has been frequently reported, but is difficult to measure directly and non-invasively in humans. Here, we examined known and novel neurophysiological measures sensitive to E–I in patients across the AD continuum. Resting-state magnetoencephalography (MEG) data of 86 amyloid-biomarker-confirmed subjects across the AD continuum (17 patients diagnosed with subjective cognitive decline, 18 with mild cognitive impairment (MCI) and 51 with dementia due to probable AD (AD dementia)), 46 healthy elderly and 20 young control subjects were reconstructed to source-space. E–I balance was investigated by detrended fluctuation analysis (DFA), a functional E/I (fE/I) algorithm, and the aperiodic exponent of the power spectrum. We found a disrupted E–I ratio in AD dementia patients specifically, by a lower DFA, and a shift towards higher excitation, by a higher fE/I and a lower aperiodic exponent. Healthy subjects showed lower fE/I ratios (< 1.0) than reported in previous literature, not explained by age or choice of an arbitrary threshold parameter, which warrants caution in interpretation of fE/I results. Correlation analyses showed that a lower DFA (E–I imbalance) and a lower aperiodic exponent (more excitation) was associated with a worse cognitive score in AD dementia patients. In contrast, a higher DFA in the hippocampi of MCI patients was associated with a worse cognitive score. This MEG-study showed E–I imbalance, likely due to increased excitation, in AD dementia, but not in early stage AD patients. To accurately determine the direction of shift in E–I balance, validations of the currently used markers and additional in vivo markers of E–I are required.

Biophysical models applied to dementia patients reveal links between geographical origin, gender, disease duration, and loss of neural inhibition

“…The balance between neural excitation and inhibition can be estimated from resting state MEG and/or EEG data by computing the aperiodic exponent of the signal. This method was validated with simulated local field potentials [ 24 ], and applied to AD patients by van Nifterick and colleagues [ 20 ], reporting signs of hyperexcitability in demented patients with AD but not in early-stage patients, which is fully consistent with our findings. Our approach is complementary to that of van Nifterick et al, as it is based on a different imaging neuroimaging modality.…”

“…While our model did not directly assess the balance between neural excitation and inhibition, previous research supports the choice of modeling changes in local inhibition in AD and bvFTD patients [ 9 , 11 , 18 , 20 , 21 ]. In turn, our results provide support for the hypothesis that alterations in neural inhibition modulated by disease-specific atrophy patterns can underlie changes in functional connectivity measured in neurodegeneration.…”

“…Although these markers are less frequent in behavioral variant FTD (bvFTD) [ 16 ], they may be also present as non-convulsive seizures [ 17 ]. The prevalence of neural excitability over inhibition is supported by transgenic mouse models of AD [ 18 ], as well as by human studies probing cortical excitability using Transcranial Magnetic Stimulation (TMS) [ 19 ], and by the analysis of spontaneous activity recorded in vivo with MEG, establishing a shift towards higher excitation in a group of advanced AD patients with dementia but not in early-stage patients [ 20 ]. Another study also reported network-specific decreases in neural inhibition in AD patients [ 21 ].…”

Assessing cortical excitability with electroencephalography: a pilot study with EEG-iTBS

“…Higher values correspond to higher E/I. Recently, it has been demonstrated that this approach is sensitive to E/I alterations known to occur in Alzheimer Disease (Van Nifterick et al 2023). The computation of DFA-based E/I was performed in alpha band, following the method described in (Van Nifterick et al 2023) and based on an adaptation for Brainstorm of the code available at https://github.com/annevannifterick/fEI_in_AD;…”

Aperiodic component of EEG power spectrum and cognitive performance in aging: the role of education

“…Compared to previous studies using the specparam algorithm, where frequency range varies, many used 40 Hz as the upper frequency range 23,48,61 . For sake of clarity, using 48 Hz as upper frequency is also a widely adopted option 62,63 , and stays within the recommendations of the algorithm’s authors. All participants showed a discernible alpha peak in the PSD (see an example in Figure 1).…”