Brain Volumetric Analysis Using Artificial Intelligence Software in Premanifest Huntington's Disease Individuals from
Margarita R Ríos-Anillo1,2, Mostapha Ahmad1, Johan E Acosta-López3
1Facultad de Ciencias de la Salud, Centro de Investigaciones en Ciencias de la Vida, Universidad Simón Bolívar, Barranquilla 080005, Colombia.
Biomedicines
|October 26, 2024
Summary
Artificial intelligence (AI) in brain imaging detects significant structural changes in premanifest Huntington's disease (HD) individuals. These changes, linked to CAG repeat expansion, appear years before motor symptoms manifest, offering early biomarker identification.
Area of Science:
- Neuroscience
- Genetics
- Medical Imaging
Background:
- Huntington's disease (HD) premanifest phase shows no motor symptoms but has detectable brain structural changes.
- Identifying these early changes is crucial for understanding disease progression and potential interventions.
Purpose of the Study:
- To analyze brain volumetric changes using AI-processed MRI in premanifest HD individuals.
- To investigate the relationship between CAG triplet expansion and structural brain biomarkers.
Main Methods:
- 36 individuals from HD-affected families underwent brain MRI.
- AI software (Entelai/IMEXHS) analyzed volumetric brain images.
- CAG trinucleotide repeats in the Huntingtin gene were quantified from blood samples.
Main Results:
- Individuals with ≥40 CAG repeats showed increased cerebrospinal fluid (CSF) and amygdalae/caudate nucleus volumes.
- Significant reductions in white matter, cerebellum, brainstem, and pallidum were observed in those with ≥40 repeats.
- Individuals with <40 repeats exhibited minimal volumetric changes.
Conclusions:
- CAG expansion selectively affects key brain regions in premanifest HD.
- AI-driven neuroimaging can identify structural biomarkers preceding clinical HD symptoms.
- These findings may aid in predicting disease trajectory and developing early interventions.
