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The Brain-Age Gap in Pediatric Dystonia: Neuroanatomical Deviations Inform Deep Brain Stimulation Outcomes
Timur H Latypov1, Alexandre Berger1, Manuela Mohareb1
1Neurosciences and Mental Health, SickKids Research Institute, Toronto, Ontario, Canada.
Insights
Brain-age gap analysis in pediatric dystonia reveals distinct subtypes and predicts outcomes. This machine learning approach may help stratify patients for better treatment targeting.
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.
Background:
Dystonia in children is a heterogeneous condition with variable response to deep brain stimulation (DBS). Brain-age gap, a machine learning-derived metric of structural deviation from norm, may capture signatures that differentiate underlying biotypes and predict outcomes.
Methods:
A brain age model was trained on several thousand normative developmental trajectories (n = 2623). Brain-age gap (the difference between predicted and chronological age) was computed from pre-DBS T1-weighted magnetic resonance imaging in 37 children with dystonia and compared to matched healthy controls. Associations with etiology, Burke-Fahn-Marsden Dystonia Rating Scale (BFMDRS) and Pediatric Quality-of-Life (PedsQL) scores were examined.
Results:
Children with dystonia showed a greater brain-age gap (structural deviation) compared to controls (P < 0.001). Greater gap was linked to worse baseline motor scores and poorer 1-year quality-of-life improvement. Patterns differed by etiology, with distinct regional deviations in genetic and acquired dystonia.
Conclusion:
Brain-age modeling reveals biologically distinct subtypes of pediatric dystonia and may offer a biomarker for stratification and outcome prediction. © 2026 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
