AI Article Synopsis

  • Age-related buildup of amyloid-β is linked to abnormal neuron activity and synchronization in Alzheimer's, as seen in both human patients and mouse models.
  • Recent research on these mouse models revealed changes in the excitability of CA1 neurons, but typically involves overexpression of mutated proteins, complicating the connection between excitability changes and amyloid accumulation.
  • By analyzing CA1 pyramidal neuron excitability in the APPPS1 mouse model through the Human Brain Project, researchers identified specific excitability features influenced by mutated proteins and amyloid levels, contributing to a more accurate computational model of Alzheimer's disease.

Article Abstract

Age-dependent accumulation of amyloid-β, provoking increasing brain amyloidopathy, triggers abnormal patterns of neuron activity and circuit synchronization in Alzheimer's disease (AD) as observed in human AD patients and AD mouse models. Recent studies on AD mouse models, mimicking this age-dependent amyloidopathy, identified alterations in CA1 neuron excitability. However, these models generally also overexpress mutated amyloid precursor protein (APP) and presenilin 1 (PS1) and there is a lack of a clear correlation of neuronal excitability alterations with progressive amyloidopathy. The active development of computational models of AD points out the need of collecting such experimental data to build a reliable disease model exhibiting AD-like disease progression. We therefore used the feature extraction tool of the Human Brain Project (HBP) Brain Simulation Platform to systematically analyze the excitability profile of CA1 pyramidal neuron in the APPPS1 mouse model. We identified specific features of neuron excitability that best correlate either with over-expression of mutated APP and PS1 or increasing Aβ amyloidopathy. Notably, we report strong alterations in membrane time constant and action potential width and weak alterations in firing behavior. Also, using a CA1 pyramidal neuron model, we evidence amyloidopathy-dependent alterations in . Finally, cluster analysis of these recordings showed that we could reliably assign a trace to its correct group, opening the door to a more refined, less variable analysis of AD-affected neurons. This inter-disciplinary analysis, bringing together experimentalists and modelers, helps to further unravel the neuronal mechanisms most affected by AD and to build a biologically plausible computational model of the AD brain.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8230571PMC
http://dx.doi.org/10.3389/fnagi.2021.668948DOI Listing

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