Joint Morphometry of Fiber Tracts and Gray Matter Structures Using Double Diffeomorphisms

Area of Science:

• Neuroimaging
• Computational Neuroscience
• Medical Image Analysis

Background:

• Understanding brain structure and connectivity is crucial for neurological research.
• Existing methods may not fully capture the complex interplay between white matter tracts and gray matter structures.
• Variability in structural connectivity is implicated in various neurological disorders.

Purpose of the Study:

• To develop and validate a novel atlas construction method for joint analysis of white matter and gray matter.
• To investigate structural connectivity variations within a population using this new method.
• To apply the method to a cohort of Tourette syndrome patients and controls.

Main Methods:

• A novel atlas construction based on a double diffeomorphism (composition of two diffeomorphisms).
• Joint optimization of diffeomorphisms to model white matter tract shape/position and gray matter correspondence.
• Approximation of fiber bundles using weighted prototypes and weighted currents metric; Bayesian estimation of atlas parameters.

Main Results:

• The method successfully models variability in structural connectivity, encompassing changes in gray matter connections and tract geometry.
• Application to Tourette syndrome patients and controls identified specific variability in the left cortico-putamen circuit.
• The atlas, deformation parameters, and noise variance were automatically estimated using a Bayesian approach.

Conclusions:

• The proposed double diffeomorphism atlas construction method offers a powerful tool for analyzing brain structural connectivity.
• This method can reveal population-specific variations in neuroanatomy and their potential link to neurological conditions.
• Findings suggest altered structural connectivity in the left cortico-putamen circuit in Tourette syndrome.