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Machine learning and regional homogeneity reveal early and subtle brain changes in type 1 diabetes
José Manuel Gómez-Barba1, Cristina Cañete-Massé2, Geisa Bearitz Gallardo-Moreno1
1Institute of Neuroscience, CUCBA, University of Guadalajara, 44130, Guadalajara, Mexico.
Summary
Type 1 diabetes mellitus (T1DM) alters brain activity, showing reduced connectivity in some areas and increased activity in others. These neural changes may be compensatory, with potential for detection using machine learning.
Area of Science:
- Neuroscience
- Endocrinology
- Medical Imaging
Background:
- Type 1 diabetes mellitus (T1DM) typically emerges in early life.
- T1DM significantly impacts cognitive processing and overall brain function.
Purpose of the Study:
- To investigate spontaneous alterations in brain activity in young adults diagnosed with T1DM.
- To compare brain activity patterns between T1DM patients and healthy controls.
Main Methods:
- Resting-state functional magnetic resonance imaging (fMRI) was employed.
- Key metrics assessed included fractional amplitude of low-frequency fluctuations (fALFF), regional homogeneity (ReHo), and voxel-mirrored homotopic connectivity (VMHC).
- Support Vector Machine (SVM) models were utilized for group discrimination.
Main Results:
- T1DM participants exhibited increased ReHo in occipital regions and decreased ReHo in cerebellar areas.
- Reduced VMHC was observed in the cerebellum and lentiform nucleus of T1DM patients.
- SVM analysis using cerebellar ReHo demonstrated significant group discrimination.
Conclusions:
- T1DM demonstrably influences spontaneous brain activity, with observed decreases in subcortical connectivity and increases in occipital activity.
- These alterations may represent disruptions and compensatory mechanisms within the brain.
- SVM shows promise for detecting neural changes associated with T1DM.
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