Mapping the contribution of clinical risk factors and MRI‐based imaging features to cognitive impairment in community elders

Abstract Background It is still not clear about the clinical risk factors of cognitive impairment in community elderlies. Although many studies have investigated the association of imaging features with cognitive impairment, none has applied location‐specific information for the prediction. Method We included 609 stroke‐ and dementia‐free elderlies with comprehensive clinical information collected (Table 1). T1‐weighted (T1W) images, T2‐weighted images, FLAIR and diffusion tensor imaging (DTI) were acquired. T1W images were processed with AccuBrain to quantify brain volumes of anatomical structures (Table 2). White matter hyperintensities (WMHs) were automatically segmented using AccuBrain and normalized to standard space for the quantification of regional burden. Other small vessel disease features, including lacunes, cerebral microbleed and enlarged perivascular spaces (EPVS) were visually rated (Table 2). Peak width of skeletonized mean diffusivity (PSMD) was calculated on DTI sequence. These vascular imaging features and clinical variables were used predict Montreal Cognitive Assessment (MoCA) and Symbol Digit Modalities Test (SDMT) with support vector regression (SVR). We compared three prediction models here: (1) with clinical variables as predictors, (2) imaging features as predictors, and (3) the combination of clinical and imaging features as predictors. Different feature selection methods were attempted. Further statistical inference was performed with permutations to investigate independent contributing factors. Result Participant characteristics were shown in Table 3. When predicting MoCA or SDMT (Figure 1), Model 3 performed no better than Model 2 or 1, and Model 1 performed better than Model 2 (p<0.001). In addition to age and education level, only average sitting systolic blood pressure presented significant independent contribution to MoCA, while for SDMT, no clinical variables had significant independent contribution (Table 4 and 5). The imaging features that had significant independent contribution included EPVS in basal ganglia, WMH volume in left superior corona radiata, and atrophy of right parietal lobe and insular for MoCA, and right insular atrophy for SDMT. Conclusion Combining clinical variables and MRI‐based features did not achieve better prediction of cognitive impairment than using either type of features alone. The highlighted predictors that presented independent contribution to MoCA or SDMT may help to understand the cognitive risk factors in community elderlies.

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