Learning to classify neural activity from a mouse model of Alzheimer's disease amyloidosis versus controls

Shlomit Beker, Vered Kellner, Gal Chechik, Edward A. Stern

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


The mechanisms underlying Alzheimer's disease (AD) onset and progression are not yet elucidated. The extent to which alterations in the activity of individual neurons of an AD model are significant, and the phase at which they can be captured, point to the intensity of the pathology and imply the stage at which it can be detected. Using a machine-learning algorithm, we present a successful cell-by-cell classification of intracellularly recorded neurons from the B6C3 APPswe/PS1dE9 AD model, versus wildtypes controls, at both a late stage and at an early stage, when the plaque pathology and behavioral deficits are absent or rare. These results suggest that the deficits present in neuronal networks of both old and young transgenic animals are large enough to be apparent at the level of individual neurons, and that the pathology could be detected in nearly any given sample, even before pathologic signs.

Original languageEnglish
Pages (from-to)39-48
Number of pages10
JournalAlzheimer's and Dementia: Diagnosis, Assessment and Disease Monitoring
StatePublished - 2016

Bibliographical note

Funding Information:
This work was supported by the National Institute on Aging at the National Institute of Aging (Grant Number AG024238 ), and the Legacy Heritage Bio-Medical Program of the Israel Science Foundation (Grant Number 688/10 ).

Publisher Copyright:
© 2016 The Authors.


  • Alzheimer's disease
  • Amyloid-β
  • Classification
  • Machine-learning
  • SVM


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