Patients suffering from Alzheimer’s disease (AD) exhibit an increased risk for developing seizures and epileptiform activity, especially at the later stages of the disease. At earlier stages of the disease, epileptiform activity is thought to be a manifestation of network hyperexcitability. Animal models of AD pathology have been suggested to exhibit similar epileptiform activity, but it remains unclear what the pathological correlates are. However, confounds associated with the presence of amyloid pathology in these animal models such as protein overexpression may influence the development of epileptiform activity. This study aimed to determine the pathological correlates of epileptiform activity in the APPKM670/671NL.PS1/L166P and APP-Knock in animal models, which do and do not exhibit protein overexpression respectively. These mice also underwent tau seeding using human-derived seeding material, and were followed across a period of 5 months after injection. We report that while APP.PS1 animals exhibit clear indications of epileptiform activity and network hyperexcitability in behavioural states of inactivity, APP-KI animals failed to show similar signs. APP.PS1 animals exhibited significantly more epileptiform activity that reduced with age, which appeared to be counteracted by tau seeding, compared to buffer and wild-type controls. Interestingly, tau seeding immediately elicited acute epileptiform activity in younger APP-KI animals, but not APP.PS1 animals or older APP-KI animals. Longitudinal effects of tau pathology were not associated with significantly increased epileptiform activity. These findings suggest that epileptiform activity in APP.PS1 animals may be associated with factors other than amyloid plaque pathology and that tau-seeding induces the development of epileptiform activity.
doi: 10.17756/jnen.2022-096
Citation: Tok S, Crauwels D, Drinkenburg W. 2022. A Characterization of Epileptiform Activity Associated
with TAU Seeding and Amyloid Pathology in the APPKM670/671NL.PS1/L166P and APP-KI Animal Models J Neurol Exp Neurosci 8(2): 18-30.
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