Combining Aperiodic 1/f Slopes and Brain Simulation: An EEG/MEG Proxy Marker of Excitation/Inhibition Imbalance in Alzheimer’s Disease

Abstract

Accumulation and interaction of amyloid-beta (Aβ) and tau proteins during progression of Alzheimer's disease (AD) are shown to tilt neuronal circuits away from balanced excitation/inhibition (E/I). Current available techniques for noninvasive interrogation of E/I in the intact human brain, e.g., magnetic resonance spectroscopy (MRS), are highly restrictive (i.e., limited spatial extent), have low temporal and spatial resolution and suffer from the limited ability to distinguish accurately between different neurotransmitters complicating its interpretation. As such, these methods alone offer an incomplete explanation of E/I. Recently, the aperiodic component of neural power spectrum, often referred to in the literature as the '1/f slope', has been described as a promising and scalable biomarker that can track disruptions in E/I potentially underlying a spectrum of clinical conditions, such as autism, schizophrenia, or epilepsy, as well as developmental E/I changes as seen in aging. Using 1/f slopes from resting-state spectral data and computational modelling we developed a new method for inferring E/I alterations in AD. We tested our method on recent freely and publicly available electroencephalography (EEG) and magnetoencephalography (MEG) datasets of patients with AD or prodromal disease and demonstrated the method's potential for uncovering regional patterns of abnormal excitatory and inhibitory parameters. Our results provide a general framework for investigating circuit-level disorders in AD and developing therapeutic interventions that aim to restore the balance between excitation and inhibition.

Viscous dynamics associated with hypoexcitation and structural disintegration in neurodegeneration via generative whole‐brain modeling

“…There are important limitations that should be addressed in future work. First, although comparable or larger than similar studies, 32,34,117 our results should be confirmed with more participants from the Global North and South. Still, the theoretical framework based on biophysical modeling may allow us to make better use of small datasets and improve the understanding of brain function by linking empirical features with biophysical mechanisms.…”

“…However, that is more likely to occur in the preclinical and early stages of dementia, during which a shift toward brain hypometabolism predominates in the middle and advanced stages 57,118–121 . Moreover, both hyperxcitation and hypoexcitation can coexist in AD, with some brain regions exhibiting increased or decreased excitability 32,34 . Further, our work is unique in combining metaconnectivity with whole‐brain modeling, which differs from other modeling studies that have reproduced empirical observables using theoretical models without previously addressing how well these features characterize neurodegenerative diseases.…”

“… 23 , 24 , 25 , 26 , 27 Despite progress, spectral and connectivity analyses often yield unclear results due to small sample sizes and variable metrics, 28 requiring large samples for reproducible results. 18 Generative brain activity models 11 may allow more robust results with moderate sample sizes, 29 , 30 , 31 provide causal mechanisms (biophysical‐inspired) 32 , 33 , 34 , 35 , and brain stimulation potential targets by in silico perturbations. 13 , 36 , 37 By generating EEG‐like signals based on the biophysical properties of local and mesoscale circuits these models can provide mechanistic interpretations and test more focused hypotheses.…”

Aperiodic neural activity is a biomarker for depression severity

“…Variations in the aperiodic slope are linked to age differences 13 , diseases 14 , and depression severity between subjects 15 . Notably, a flattening of the aperiodic slope is observed in patients receiving pharmacological treatment for MDD 16 , though this study did not distinguish between responders and non-responders.…”

“…As a ‘snapshot’ of activity across all frequency bands, we hypothesize that aperiodic slope in the affective network may represent the degree of effective coordination of activity across the distributed brain regions involved in maintaining healthy affect. Variations in the aperiodic slope are linked to age differences 13 , diseases 14 , and depression severity between subjects 15 . Notably, a flattening of the aperiodic slope is observed in patients receiving pharmacological treatment for MDD 16 , though this study did not distinguish between responders and non-responders.…”