Functional connectivity (FC) has long been the gold standard for understanding how different brain regions communicate. However, traditional FC often assumes these connections are static over the course of a scan, averaging activity over 5-10 minutes. This approach—while useful—collapses the rich temporal dynamics of the brain into a single correlation matrix.

My research in the MIND Lab challenges this assumption by looking at Quasi-Periodic Patterns (QPPs)—recurring spatiotemporal events that drive much of the brain’s functional architecture.

The “Static” Limitation

When you average brain activity, you lose the movie of the brain. It’s like trying to understand a symphony by looking at a long-exposure photograph of the orchestra. You see who sits near whom (topology), but you miss the music (dynamics). In neurodegenerative diseases like Alzheimer’s, the timing of neural communication often breaks down long before the structural “wiring” disappears.

Study Design: TG2576 Mouse Model

In our recent study (LaGrow et al.), we utilized resting-state fMRI (rsfMRI) to examine QPPs in TG2576 mice (a model of amyloid pathology) compared to age-matched controls. We used a pattern-finding algorithm that iteratively identifies recurring spatiotemporal templates in the BOLD signal.

Key Findings

  1. DMN-TPN Anti-correlation: In healthy brains, the Default Mode Network (DMN) and Task-Positive Network (TPN) are strongly anti-correlated—when one is on, the other is off.
  2. Pathological Breakdown: TG2576 mice showed a specific breakdown in this dynamic. The QPPs revealed significantly reduced anti-correlation between DMN and TPN regions compared to controls. The networks began to “blur” together dynamically.
  3. Superior Classification: Crucially, we found that metrics derived from QPPs significantly improved the classification of Alzheimer’s pathology compared to conventional static FC measures.

Why This Matters

This suggests that dynamic biomarkers (like QPP strength and phase-locking) could serve as earlier warning signs than structural MRI changes or even static FC disruptions. By treating the brain as a dynamic system rather than a static graph, we can detect subtle network failures that traditional methods miss.

Read the full paper: Link to Scholar/Paper