Morphometric similarity networks (MSNs) estimate organization of the cortex as a biologically meaningful set of similarities between anatomical features at the macro- and microstructural level, derived from multiple structural MRI (sMRI) sequences. These networks are clinically relevant, predicting 40% variance in IQ. However, the sequences required (T1w, T2w, DWI) to produce these networks are longer acquisitions, less feasible in some populations. Thus, estimating MSNs using features from T1w sMRI is attractive to clinical and developmental neuroscience. We studied whether reduced-feature approaches approximate the original MSN model as a potential tool to investigate brain structure. In a large, homogenous dataset of healthy young adults (from the Human Connectome Project, HCP), we extended previous investigations of reduced-feature MSNs by comparing not only T1w-derived networks, but also additional MSNs generated with fewer MR sequences, to their full acquisition counterparts. We produce MSNs that are highly similar at the edge level to those generated with multimodal imaging; however, the nodal topology of the networks differed. These networks had limited predictive validity of generalized cognitive ability. Overall, when multimodal imaging is not available or appropriate, T1w-restricted MSN construction is feasible, provides an appropriate estimate of the MSN, and could be a useful approach to examine outcomes in future studies. Author Summary We can estimate the higher order organization of cortical gray matter as a connectome using structural MRI. However, this methodology, termed morphometric similarity, requires multiple advanced neuroimaging protocols that are unsuitable, unavailable, or intolerable to certain populations, including children and some clinical groups. In a large, homogenous dataset of healthy young adults, we estimated these connectomes using three different feature sets, each extracted from fewer MRI sequences. Even when produced using only T1-weighted structural MRI scans, these connectomes were broadly similar to those produced with more complex or numerous MRI sequences. We did not replicate previous findings linking variation in the morphometric similarity networks (MSNs) to individual differences in cognitive abilities. We highlight potential reasons for this, including the developmental stage of the young adult imaging cohort in which our hypotheses were tested, and conclude that this study provides putative evidence that, in those populations where advanced imaging is not plausible, MSNs generated from T1-weighted structural MRIs are a promising alternative.