Predictive Model of Spread of Progressive Supranuclear Palsy Using Directional Network Diffusion

Area of Science:

• Neuroscience
• Neuropathology
• Computational Biology

Background:

• Neurodegenerative diseases like Alzheimer's (AD), frontotemporal dementia (FTD), and Parkinson's (PD) involve pathogenic protein spread between brain cells.
• Disease progression in these disorders follows stereotyped regional patterns, often linked to misfolded tau or alpha-synuclein.
• Progressive supranuclear palsy (PSP) is a less-studied synucleinopathy with stereotyped pathology initially affecting the striatum and connected cortical areas.

Purpose of the Study:

• To investigate if the network diffusion model (NDM) applies to PSP pathology.
• To determine if directional information in brain networks enhances NDM accuracy for PSP.
• To explore the role of the brain's structural network architecture in mediating PSP spread.

Main Methods:

• Applied the connectome-based network diffusion model (NDM) to progressive supranuclear palsy (PSP) data.
• Compared NDM fit using directional (anterograde and retrograde) versus non-directional human connectomes.
• Analyzed the topography and progression of PSP pathology within brain circuits.

Main Results:

• PSP pathology is amenable to connectome-based network diffusion modeling, similar to AD and FTD.
• NDM accuracy for PSP is significantly improved by incorporating directional information from the human connectome.
• Both anterograde and retrograde transmission models explained PSP topography more accurately than non-directional models.

Conclusions:

• The intrinsic architecture of the brain's structural network mediates disease spread in PSP.
• Trans-neuronal transmission is a likely mechanism driving PSP progression.
• Directional connectome data are crucial for accurately modeling neurodegenerative disease spread.