Related Experiment Video
Updated: May 21, 2025
Modeling the Functional Network for Spatial Navigation in the Human Brain
Published on: October 13, 2023
Connectome-based predictive modeling of brain pathology and cognition in autosomal dominant Alzheimer's disease
Vaibhav Tripathi1,2, Joshua Fox-Fuller1,3, Vincent Malotaux3
1Department of Psychological and Brain Sciences, Boston University, Boston, Massachusetts, USA.
Introduction:
Autosomal dominant Alzheimer's disease (ADAD) through genetic mutations can result in near complete expression of the disease. Tracking AD pathology development in an ADAD cohort of Presenilin-1 (PSEN1) E280A carriers' mutation has allowed us to observe incipient tau tangles accumulation as early as 6 years prior to symptom onset.
Methods:
Resting-state functional magnetic resonance imaging (fMRI) and positron emission tomography (PET) scans were acquired in a group of PSEN1 carriers (n = 32) and non-carrier family members (n = 35). We applied connectome-based predictive modeling (CPM) to examine the relationship between the participant's functional connectome and their respective tau/amyloid-β levels and cognitive scores (word list recall).
Results:
CPM models strongly predicted tau concentrations and cognitive scores within the carrier group. The connectivity patterns between the temporal cortex, default mode network, and other memory networks were the most informative of tau burden.
Discussion:
These results indicate that resting-state functional magnetic resonance imaging (fMRI) methods can complement PET methods in early detection and monitoring of disease progression in ADAD.
Highlights:
Connectivity-based predictive modeling of tau and amyloid-β in ADAD carriers. Strong predictions for tau deposition; weaker predictions for amyloid-β. Cognitive scores for memory and mental state are predicted strongly. Connectivity between IPL, DAN, DMN, temporal cortex most predictive.
