Detect the disrupted brain structural connectivity in type 2 diabetes mellitus patients without cognitive impairment

Table 3 The 18 selected connectivity features for type 2 diabetes mellitus classification models

Connections	Ridge regression coefficients
Parietal_Inf_R-SupraMarginal_R	3.3436
Supp_Motor_Area_R-Cingulum_Mid_R	3.1377
Cuneus_L-Caudate_L	2.0824
Frontal_Sup_Orb_L-Parietal_Inf_L	1.7015
Supp_Motor_Area_L-Supp_Motor_Area_R	1.5698
Frontal_Mid_Orb_R-Thalamus_R	1.5043
Calcarine_R-Thalamus_R	1.3564
Supp_Motor_Area_R-Paracentral_Lobule_L	1.3261
Frontal_Med_Orb_R-Cingulum_Mid_R	1.1464
Parietal_Inf_R-Temporal_Inf_R	0.9017
ParaHippocampal_R-Occipital_Sup_R	0.8107
Frontal_Med_Orb_L-Pallidum_L	0.8031
Calcarine_L-Fusiform_L	0.7449
Caudate_L-Pallidum_L	0.6747
Frontal_Sup_Medial_R-Putamen_R	0.5617
Frontal_Med_Orb_L-Putamen_L	0.5418
Fusiform_R-Thalamus_R	0.4652
Cingulum_Post_R-Occipital_Sup_L	0.3565

Please refer to the AAL-116 template for the names and locations of the brain regions of interest listed in the table. The table displays the absolute values of the coefficients for each feature (sorted from highest to lowest) in the model with the best performance, the Ridge regression model. A larger coefficient value indicates a greater contribution of the feature to the prediction outcome. The connections associated with the deep subcortical nuclei were highlighted in green.