Alzheimer s & Dementia Diagnosis Assessment & Disease MonitoringNeurosciencePeer-reviewed journal article
Combining Aperiodic 1/f Slopes and Brain Simulation: An EEG/MEG Proxy Marker of Excitation/Inhibition Imbalance in Alzheimer's Disease
Pablo Martínez‐CañadaiD, Eduardo Perez‐ValeroiD, Jesús MinguillóniD, Francisco J. PelayoiD, M. A. López-GordoiD, Christian A. MorillasiD
Abstract
Accumulation and interaction of amyloid-beta (A{beta}) and tau proteins during progression of Alzheimers 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 methods 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.
- organism: human
- assay: electronic health records
- research focus: benchmarking
- approach: statistical model
- Citations
- 52
- FWCI
- 7.79
- Field percentile
- top 1%
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