David Darrow, MD

2 days ago

New episode in our trigeminal neuralgia guideline stakeholder series.

Vishad Sukul and I spoke with Ajay Malhotra about how imaging shapes diagnosis and surgical decision-making in trigeminal neuralgia.

We discuss:

• dedicated trigeminal MRI protocols
• neurovascular contact vs compression
• secondary causes of trigeminal neuralgia
• MS-related trigeminal neuralgia
• recurrence after MVD
• imaging standardization and AI

This conversation is part of the AANS/CNS Joint Section on Pain effort to develop evidence-based surgical guidelines for trigeminal neuralgia. The goal is to include stakeholder perspectives early, before finalizing the questions that will guide the systematic review.

Full episode at the link in bio.

#trigeminalneuralgia #neuroradiology #MRI #FacialPain #neurosurgery PainMedicine EvidenceBasedMedicine AANS CNS Neuromodulation
@the_asnr … See MoreSee Less

David Darrow, MD is at University of Minnesota Twin Cities.

2 days ago

Brain disorders are network disorders.

Epilepsy, depression, Parkinson’s disease, traumatic brain injury, and other neurologic and psychiatric conditions may begin in one brain region — but their effects can spread across connected brain networks.

The challenge is that the “map” we build of those networks depends on the metric we choose.

Our new open-access paper in Journal of Neural Engineering tests commonly used effective connectivity metrics against simulated brain networks where the true connections were known. We asked a practical question: which methods actually reconstruct the network accurately when the data include real-world challenges like noise, limited recording time, incomplete network coverage, and time-delayed communication?
The key takeaway: metrics matter.

When brain regions communicate with time delays, zero-lag methods can miss the true network. Multivariate transfer entropy was the most reliable overall, but computationally slow. Mutual information and Granger causality performed well in smaller networks, while partial cross-correlation was useful for larger networks.
In other words: choosing the wrong metric is like trying to understand disease using a map with made-up streets.
Better metrics can help us build better brain-network maps — and better maps can help us better understand brain disease.

Paper: Metric validation for detection of delayed and directed coupling
Published in Journal of Neural Engineering
DOI: 10.1088/1741-2552/ae5fd7
#Neuroscience #BrainNetworks #NetworkNeuroscience #Neuroengineering #Neurosurgery Psychiatry ComputationalNeuroscience EffectiveConnectivity BrainResearch UMN … See MoreSee Less