Linear diffusion modeling of tau pathology spread

By J Kate Brynildsen, PhD, Michael Henderson, PhD and Danielle (Dani) Bassett, PhD
View Published Code

Output Details

Description
Code to reproduce all network analyses in Lubben et al. "LRRK2 kinase inhibition reverses G2019S mutation-dependent effects on tau pathology spread".
Tags
  • Computer modeling
  • Network analysis
  • Predictive models
Team
Team Biederer
Program
Collaborative Research Network
Theme
Circuitry and Brain-Body Interactions

Meet the Authors

Related Research

LRRK2 kinase inhibition reverses G2019S mutation-dependent effects on tau pathology spread

Mutations in LRRK2 are the most common cause of familial Parkinson’s disease. This work supports a protective role of LRRK2 kinase inhibition in G2019S carriers and provides a workflow for systematic evaluation of brain-wide phenotypes

View Article

Network analysis of α-synuclein pathology progression reveals p21-activated kinases as regulators of vulnerability

Misfolded α-synuclein accumulation in PD leads to pathogenic processes. We mapped α-synuclein pathology in mice to identify vulnerable regions and potential therapeutic targets, highlighting group II PAK inhibitors as promising for treating PD.

View Article

Simulations predict differing phase responses to excitation vs. inhibition intheta-resonant pyramidal neurons

This research highlights how cortical neurons respond differently to oscillatory inputs, impacting neuronal responses to network state shifts.

View Article
View More Outputs