Machine learning and regional homogeneity reveal early and subtle brain changes in type 1 diabetes

Type 1 diabetes mellitus (T1DM) usually begins early in life, and its development impacts brain functioning and cognitive processing. The present study examined alterations in spontaneous brain activity in young adults with T1DM.

Thirty-five T1DM participants and thirty-five matched healthy controls underwent resting-state functional magnetic resonance imaging to assess the fractional amplitude of low-frequency fluctuations (fALFF), regional homogeneity (ReHo), and voxel-mirrored homotopic connectivity (VMHC). Between-group comparisons and correlation analyses were performed and corrected for multiple comparisons via Gaussian Random Field theory (voxel p < .001, cluster p < .025). Support Vector Machine (SVM) models were trained using cluster-derived features identified in group comparisons, and performance was evaluated on a test set.

The T1DM group showed increased ReHo values in occipital lobe areas and reduced ReHo values in cerebellar areas. Within the T1DM group, ReHo values in occipital regions were positively associated with intelligence quotient. VMHC analyses revealed decreased interhemispheric connectivity in the cerebellum and lentiform nucleus in T1DM participants relative to controls. No significant between-group differences in fALFF were observed. When cerebellar ReHo values were used as features, the SVM classifier achieved modest yet statistically significant discrimination between groups.

T1DM influences spontaneous brain activity. Decreases in subcortical VMHC and ReHo suggest potential disruptions in connectivity, whereas increases in occipital ReHo may be consistent with a compensatory mechanism. The use of SVM provides preliminary evidence of its potential utility for future research aimed at detecting neural changes in this population.