• VSN reflects the cytoarchitectonic organization of the human brain. • Individual VSN enhances accurate brain fingerprint identification. • The correlation-based method is recommended for standardized VSN construction. • VSN fails to predict attention, unlike functional connectomes. The T1-weighted brain magnetic resonance imaging (MRI)-based volumetric similarity network (VSN) offers an advantage in clinical settings due to its ease of acquisition and widespread availability. However, its validity, reliability, and behavioral relevance remain unclear. The present study aimed to assess the reproducibility and utility of the VSN as a foundation for future research and clinical applications. Here, we analyzed three datasets (total N = 354), with two datasets having repeated MR runs (Dataset 1: n = 86; Dataset 2: n = 49) and two having an attention measure (Datasets 1 and 3: n = 219). For each run and participant, the VSN was generated using interregional morphological similarity metrics. We examined whether the VSN reflects the brain’s cytoarchitecture and assessed its test–retest reliability by using connectome fingerprints in Datasets 1 and 2. We also examined the VSN’s behavioral relevance and further tested its predictive utility using connectome-based predictive modeling in Datasets 1 and 3. The VSN defined using the z-transformed interregional correlation showed significant spatial similarity with the cytoarchitectonic covariance network ( rhos = 0.23 and 0.22 in Datasets 1 and 2, respectively; p 0.31, p 0.3). This study demonstrates the biological validity and high reliability of the VSN to support brain fingerprinting of individual subjects, but not individual differences in attention.
Kim et al. (Sun,) studied this question.