Abstract Constructing structural similarity networks from T1-weighted MRI offers a powerful means to characterize brain organization. Two prominent methods for constructing such networks, Morphometric Similarity Network (MSN) and Morphometric INverse Divergence (MIND), have been proposed. However, a systematic evaluation of the test-retest reliability and age sensitivity of both MIND and MSN is still lacking. The present study comprehensively assessed these properties to inform the reliability and validity of both approaches. Test-retest reliability was evaluated by the intraclass correlation coefficient (ICC) using two public datasets containing repeated MRI scans. Age sensitivity was examined by conducting edge-wise comparisons between younger and older age groups, as well as by training machine learning models to predict individual age, using two public lifespan datasets. Additionally, several practical variants of MIND and MSN were explored by constructing networks with different morphological feature sets. Results demonstrated that MSN exhibited higher test-retest reliability, whereas MIND showed greater age sensitivity when both methods employed the same five features. Both methods revealed distinct spatial patterns that differentiate older from younger adults. Notably, the choice of feature sets substantially influenced reliability and age sensitivity. These findings offer empirical guidance for methodological selection and highlight the importance of feature optimization in future studies.
Gao et al. (Tue,) studied this question.
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