The Brain-Age Gap in Pediatric Dystonia: Neuroanatomical Deviations Inform Deep Brain Stimulation Outcomes

Area of Science:

• Neuroscience
• Machine Learning
• Pediatric Neurology

Background:

• Pediatric dystonia is a complex neurological disorder with varied responses to treatments like deep brain stimulation (DBS).
• Identifying distinct patient subgroups is crucial for personalized treatment strategies.
• Brain-age gap, a metric reflecting structural brain deviation, shows promise in characterizing neurological conditions.

Purpose of the Study:

• To investigate if brain-age gap can differentiate subtypes of pediatric dystonia.
• To explore the association between brain-age gap and clinical outcomes in children with dystonia.
• To assess the potential of brain-age gap as a biomarker for pediatric dystonia.

Main Methods:

• A brain age model was developed using normative developmental trajectories (n=2623).
• Brain-age gap was calculated from pre-treatment MRI scans in 37 children with dystonia and compared to controls.
• Correlations between brain-age gap, dystonia etiology, motor function (BFMDRS), and quality of life (PedsQL) were analyzed.

Main Results:

• Children with dystonia exhibited a significantly larger brain-age gap compared to healthy controls (P < 0.001).
• A greater brain-age gap correlated with poorer baseline motor scores and less improvement in quality of life after one year.
• Distinct patterns of regional brain deviation were observed between genetic and acquired dystonia subtypes.

Conclusions:

• Brain-age modeling effectively identifies biologically distinct subtypes within pediatric dystonia.
• The brain-age gap may serve as a valuable imaging biomarker for stratifying patients and predicting treatment outcomes.
• This machine learning approach offers a novel perspective on understanding the heterogeneity of pediatric dystonia.